SwePub - sökning: WFRF:(Xia Yuxuan 1993)

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1.	Beal, Jacob, et al. (författare) Robust estimation of bacterial cell count from optical density 2020 Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1 Tidskriftsartikel (refereegranskat)abstract Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
2.	Garcaa-Fernandez, Angel F., et al. (författare) Gaussian implementation of the multi-Bernoulli mixture filter 2019 Ingår i: FUSION 2019 - 22nd International Conference on Information Fusion. Konferensbidrag (refereegranskat)abstract This paper presents the Gaussian implementation of the multi-Bernoulli mixture (MBM) filter. The MBM filter provides the filtering (multi-target) density for the standard dynamic and radar measurement models when the birth model is multi-Bernoulli or multi-Bernoulli mixture. Under linear/Gaussian models, the single target densities of the MBM mixture admit Gaussian closed-form expressions. Murty's algorithm is used to select the global hypotheses with highest weights. The MBM filter is compared with other algorithms in the literature via numerical simulations.
3.	Garcia, Angel, 1984, et al. (författare) A comparison between PMBM Bayesian track initiation and labelled RFS adaptive birth 2022 Ingår i: 2022 25th International Conference on Information Fusion, FUSION 2022. ; , s. 1143-1150 Konferensbidrag (refereegranskat)abstract This paper provides a comparative analysis between the adaptive birth model used in the labelled random finite set literature and the track initiation in the Poisson multi-Bernoulli mixture (PMBM) filter, with point-target models. The PMBM track initiation is obtained via Bayes' rule applied on the pre-dicted PMBM density, and creates one Bernoulli component for each received measurement, representing that this measurement may be clutter or a detection from a new target. Adaptive birth mimics this procedure by creating a Bernoulli component for each measurement using a different rule to determine the probability of existence and a user-defined single-target density. This paper first provides an analysis of the differences that arise in track initiation based on isolated measurements. Then, it shows that adaptive birth underestimates the number of objects present in the surveillance area under common modelling assumptions. Finally, we provide numerical simulations to further illustrate the differences.
4.	Garcia, Angel, 1984, et al. (författare) A Poisson multi-Bernoulli mixture filter for coexisting point and extended targets 2021 Ingår i: IEEE Transactions on Signal Processing. - 1941-0476 .- 1053-587X. ; 69, s. 2600-2610 Tidskriftsartikel (refereegranskat)abstract This paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter for coexisting point and extended targets, i.e., for scenarios where there may be simultaneous point and extended targets. The PMBM filter provides a recursion to compute the multi-target filtering posterior based on probabilistic information on data associations, and single-target predictions and updates. In this paper, we first derive the PMBM filter update for a generalised measurement model, which can include measurements originated from point and extended targets. Second, we propose a single-target space that accommodates both point and extended targets and derive the filtering recursion that propagates Gaussian densities for single targets and gamma Gaussian inverse Wishart densities for extended targets. As a computationally efficient approximation of the PMBM filter, we also develop a Poisson multi-Bernoulli (PMB) filter for coexisting point and extended targets. The resulting filters are analysed via numerical simulations.
5.	Garcia, Angel, 1984, et al. (författare) Poisson multi-Bernoulli mixture filter with general target-generated measurements and arbitrary clutter 2023 Ingår i: IEEE Transactions on Signal Processing. - 1941-0476 .- 1053-587X. ; 71, s. 1895-1906 Tidskriftsartikel (refereegranskat)abstract This paper shows that the Poisson multi-Bernoulli mixture (PMBM) density is a multi-target conjugate prior for general target-generated measurement distributions and arbitrary clutter distributions. That is, for this multi-target measurement model and the standard multi-target dynamic model with Poisson birth model, the predicted and filtering densities are PMBMs. We derive the corresponding PMBM filtering recursion. Based on this result, we implement a PMBM filter for point-target measurement models and negative binomial clutter density in which data association hypotheses with high weights are chosen via Gibbs sampling. We also implement an extended target PMBM filter with clutter that is the union of Poisson-distributed clutter and a finite number of independent clutter sources. Simulation results show the benefits of the proposed filters to deal with non-standard clutter.
6.	Garcia, Angel, 1984, et al. (författare) Trajectory multi-Bernoulli filters for multi-target tracking based on sets of trajectories 2020 Ingår i: Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020. ; , s. 313-320 Konferensbidrag (refereegranskat)abstract This paper presents two multi-Bernoulli filters on sets of trajectories for multiple target tracking. The first filter provides a multi-Bernoulli approximation of the posterior density over the set of alive trajectories at the current time step. The second filter provides a multi-Bernoulli approximation of the posterior density over the set of all trajectories (alive and dead) up to the current time. We also explain the Gaussian implementation of the filters and compare them with other multiple target tracking algorithms in a simulated scenario.
7.	Granström, Karl, 1981, et al. (författare) Likelihood-Based Data Association for Extended Object Tracking Using Sampling Methods 2018 Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 3:1, s. 30-45 Tidskriftsartikel (refereegranskat)abstract Environment perception is a key enabling technology in autonomous vehicles, and multiple object tracking is an important part of this. The use of high resolution sensors, such as automotive radar and lidar, leads to the extended object tracking problem, with multiple detections per tracked object. For computationally feasible multiple extended object tracking, the data association problem must be handled. Previous work has relied on a two-step approach, using clustering algorithms, together with assignment algorithms, to achieve this. In this paper, we show that it is possible to handle the data association in a single step that works directly on the desired likelihood function. Single step data association is beneficial, because it enables better use of the measurement model and the predicted multiobject density. For single step data association, we use algorithms based on stochastic sampling, and integrate them into a Poisson Multi-Bernoulli Mixture filter. In a simulation study, and in an experiment with Velodyne data acquired in an urban environment, four sampling algorithms are compared to clustering and assignment. The results from the simulations and the experiment show that single-step likelihood-based data association achieves better performance than two-step clustering and assignment data association does.
8.	Granström, Karl, 1981, et al. (författare) Poisson Multi-Bernoulli Mixture Trackers: Continuity Through Random Finite Sets of Trajectories 2018 Ingår i: 2018 21st International Conference on Information Fusion, FUSION 2018. ; , s. 973-981 Konferensbidrag (refereegranskat)abstract The Poisson multi-Bernoulli mixture (PMBM) is an unlabelled multi-target distribution for which the prediction and update are closed. It has a Poisson birth process, and new Bernoulli components are generated on each new measurement as a part of the Bayesian measurement update. The PMBM filter is similar to the multiple hypothesis tracker (MHT), but seemingly does not provide explicit continuity between time steps. This paper considers a recently developed formulation of the multi-target tracking problem as a random finite set (RFS) of trajectories, and derives two trajectory RFS filters, called PMBM trackers. The PMBM trackers efficiently estimate the set of trajectories, and share hypothesis structure with the PMBM filter. By showing that the prediction and update in the PMBM filter can be viewed as an efficient method for calculating the time marginals of the RFS of trajectories, continuity in the same sense as MHT is established for the PMBM filter.
9.	Granström, Karl, 1981, et al. (författare) Spatiotemporal Constraints for Sets of Trajectories with Applications to PMBM Densities 2020 Ingår i: Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020. ; , s. 343-350 Konferensbidrag (refereegranskat)abstract In this paper we introduce spatiotemporal constraints for trajectories, i.e., restrictions that the trajectory must be in some part of the state space (spatial constraint) at some point in time (temporal constraint). Spatiotemporal contraints on trajectories can be used to answer a range of important questions, including, e.g., “where did the person that were in area A at time t, go afterwards?”. We discuss how multiple constraints can be combined into sets of constraints, and we then apply sets of constraints to set of trajectories densities, specifically Poisson Multi-Bernoulli Mixture (PMBM) densities. For Poisson target birth, the exact posterior density is PMBM for both point targets and extended targets. In the paper we show that if the unconstrained set of trajectories density is PMBM, then the constrained density is also PMBM. Examples of constrained trajectory densities motivate and illustrate the key results.
10.	Kim, Hyowon, et al. (författare) Set-Type Belief Propagation with Applications to Poisson Multi-Bernoulli SLAM 2024 Ingår i: IEEE Transactions on Signal Processing. - 1941-0476 .- 1053-587X. ; 72, s. 1989-2005 Tidskriftsartikel (refereegranskat)abstract Belief propagation (BP) is a useful probabilistic inference algorithm for efficiently computing approximate marginal probability densities of random variables. However, in its standard form, BP is only applicable to the vector-type random variables with a fixed and known number of vector elements, while certain applications rely on random finite sets (RFSs) with an unknown number of vector elements. In this paper, we develop BP rules for factor graphs defined on sequences of RFSs where each RFS has an unknown number of elements, with the intention of deriving novel inference methods for RFSs. Furthermore, we show that vector-type BP is a special case of set-type BP, where each RFS follows the Bernoulli process. To demonstrate the validity of developed set-type BP, we apply it to the Poisson multi-Bernoulli (PMB) filter for simultaneous localization and mapping (SLAM), which naturally leads to a set-type BP PMB-SLAM method, which is analogous to a vector type SLAM method, subject to minor modifications.
11.	Li, Lechi, et al. (författare) Deep Fusion of Multi-Object Densities Using Transformer 2023 Ingår i: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 1520-6149. ; 2023 Konferensbidrag (refereegranskat)abstract The fusion of multiple probability densities has important applications in many fields, including, for example, multi-sensor signal processing, robotics, and smart environments. In this paper, we demonstrate that deep learning based methods can be used to fuse multi-object densities. Given a scenario with several sensors with possibly different field-of-views, tracking is performed locally in each sensor by a tracker, which produces random finite set multi-object densities. To fuse outputs from different trackers, we adapt a recently proposed transformer-based multi-object tracker, where the fusion result is a global multi-object density, describing the set of all alive objects at the current time. We compare the performance of the transformer-based fusion method with a well-performing model-based Bayesian fusion method in several simulated scenarios with different parameter settings using synthetic data. The simulation results show that the transformer-based fusion method outperforms the model-based Bayesian method in our experimental scenarios. The code is available at https://github.com/Lechili/DeepFusion.
12.	Liu, Jianan, et al. (författare) Deep Instance Segmentation with Automotive Radar Detection Points 2023 Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 8:1, s. 84-94 Tidskriftsartikel (refereegranskat)abstract Automotive radar provides reliable environmental perception in all-weather conditions with affordable cost, but it hardly supplies semantic and geometry information due to the sparsity of radar detection points. With the development of automotive radar technologies in recent years, instance segmentation becomes possible by using automotive radar. Its data contain contexts such as radar cross section and micro-Doppler effects, and sometimes can provide detection when the field of view is obscured. The outcome from instance segmentation could be potentially used as the input of trackers for tracking targets. The existing methods often utilize a clustering-based classification framework, which fits the need of real-time processing but has limited performance due to minimum information provided by sparse radar detection points. In this paper, we propose an efficient method based on clustering of estimated semantic information to achieve instance segmentation for the sparse radar detection points. In addition, we show that the performance of the proposed approach can be further enhanced by incorporating the visual multi-layer perceptron. The effectiveness of the proposed method is verified by experimental results on the popular RadarScenes dataset, achieving 89.53% mean coverage and 86.97% mean average precision with the IoU threshold of 0.5, which is superior to other approaches in the literature. More significantly, the consumed memory is around 1MB, and the inference time is less than 40ms, indicating that our proposed algorithm is storage and time efficient. These two criteria ensure the practicality of the proposed method in real-world systems.
13.	Liu, Jianan, et al. (författare) GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles 2023 Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 8:2, s. 1176-1189 Tidskriftsartikel (refereegranskat)abstract Multi-object tracking (MOT) is among crucial applications in modern advanced driver assistance systems (ADAS) and autonomous driving (AD) systems. The global nearest neighbor (GNN) filter, as the earliest random vector-based Bayesian tracking framework, has been adopted in most of state-of-the-arts trackers in the automotive industry. The development of random finite set (RFS) theory facilitates a mathematically rigorous treatment of the MOT problem, and different variants of RFS-based Bayesian filters have then been proposed. However, their effectiveness in the real ADAS and AD application is still an open problem. In this paper, it is demonstrated that the latest RFS-based Bayesian tracking framework could be superior to typical random vector-based Bayesian tracking framework via a systematic comparative study of both traditional random vector-based Bayesian filters with rule-based heuristic track maintenance and RFS-based Bayesian filters on the nuScenes validation dataset. An RFS-based tracker, namely Poisson multi-Bernoulli filter using the global nearest neighbor (GNN-PMB), is proposed to LiDAR-based MOT tasks. This GNN-PMB tracker is simple to use, and it achieves competitive results on the nuScenes dataset. Specifically, the proposed GNN-PMB tracker outperforms most state-of-the-art LiDAR-only trackers and LiDAR and camera fusion-based trackers, ranking the $3^{rd}$ among all LiDAR-only trackers on nuScenes 3D tracking challenge leader board 1 1 https://bit.ly/3bQJ2CP at the time of submission. Our code is available at https://github.com/chisyliu/GnnPmbTracker .
14.	Pinto, Juliano, 1990, et al. (författare) An Uncertainty-Aware Performance Measure for Multi-Object Tracking 2021 Ingår i: IEEE Signal Processing Letters. - 1070-9908 .- 1558-2361. ; 28, s. 1689-1693 Tidskriftsartikel (refereegranskat)abstract Evaluating the performance of multi-object tracking (MOT) methods is not straightforward, and existing performance measures fail to consider all the available uncertainty information in the MOT context. This can lead practitioners to select models which produce uncertainty estimates of lower quality, negatively impacting any downstream systems that rely on them. Additionally, most MOT performance measures have hyperparameters, which makes comparisons of different trackers less straightforward. We propose the use of the negative log-likelihood (NLL) of the multi-object posterior given the set of ground-truth objects as a performance measure. This measure takes into account all available uncertainty information in a sound mathematical manner without hyperparameters. We provide efficient algorithms for approximating the computation of the NLL for several common MOT algorithms, show that in some cases it decomposes and approximates the widely-used GOSPA metric, and provide several illustrative examples highlighting the advantages of the NLL in comparison to other MOT performance measures.
15.	Pinto, Juliano, 1990, et al. (författare) Deep Learning for Model-Based Multi-Object Tracking 2023 Ingår i: IEEE Transactions on Aerospace and Electronic Systems. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 1557-9603 .- 0018-9251. ; 59:6, s. 7363-7379 Tidskriftsartikel (refereegranskat)abstract Multi-object tracking (MOT) is the problem of tracking the state of an unknown and time-varying number of objects using noisy measurements, with important applications such as autonomous driving, tracking animal behavior, defense systems, and others. The MOT task can be divided into two settings, model-based or model-free, depending on whether accurate and tractable models of the environment are available. Model-based MOT has Bayes-optimal closed-form solutions which can achieve state-of-the-art (SOTA) performance. However, these methods require approximations in challenging scenarios to remain tractable, which impairs their performance. Deep learning (DL) methods offer a promising alternative, but existing DL models are almost exclusively designed for a model-free setting and are not easily translated to the model-based setting. This paper proposes a DL-based tracker specifically tailored to the model-based MOT setting and provides a thorough comparison to SOTA alternatives. We show that our DL-based tracker is able to match performance to the benchmarks in simple tracking tasks while outperforming the alternatives as the tasks become more challenging. These findings provide strong evidence of the applicability of DL also to the model-based setting, which we hope will foster further research in this direction.
16.	Pinto, Juliano, 1990, et al. (författare) Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep Learning 2021 Ingår i: Proceedings of 2021 IEEE 24th International Conference on Information Fusion, FUSION 2021. ; , s. 1059-1066 Konferensbidrag (refereegranskat)abstract Multitarget Tracking (MTT) is the problem of tracking the states of an unknown number of objects using noisy measurements, with important applications to autonomous driving, surveillance, robotics, and others. In the model-based Bayesian setting, there are conjugate priors that enable us to express the multi-object posterior in closed form, which could theoretically provide Bayes-optimal estimates. However, the posterior involves a super-exponential growth of the number of hypotheses over time, forcing state-of-the-art methods to resort to approximations for remaining tractable, which can impact their performance in complex scenarios. Model-free methods based on deep-learning provide an attractive alternative, as they can, in principle, learn the optimal filter from data, but to the best of our knowledge were never compared to current state-of-the-art Bayesian filters, specially not in contexts where accurate models are available. In this paper, we propose a high-performing deeplearning method for MTT based on the Transformer architecture and compare it to two state-of-the-art Bayesian filters, in a setting where we assume the correct model is provided. Although this gives an edge to the model-based filters, it also allows us to generate unlimited training data. We show that the proposed model outperforms state-of-the-art Bayesian filters in complex scenarios, while matching their performance in simpler cases, which validates the applicability of deep-learning also in the model-based regime. The code for all our implementations is made available at https://github.com/JulianoLagana/MT3.
17.	Xia, Yuxuan, 1993, et al. (författare) An Efficient Implementation of the Extended Object Trajectory PMB Filter Using Blocked Gibbs Sampling 2023 Ingår i: 2023 26th International Conference on Information Fusion, FUSION 2023. Konferensbidrag (refereegranskat)abstract This paper presents an efficient implementation of the trajectory Poisson multi-Bernoulli (PMB) filter for multiple extended object tracking (EOT), which directly estimates a set of object trajectories. The trajectory PMB filter propagates a PMB density on the posterior of sets of trajectories through the filtering recursions over time, where the multi-Bernoulli (MB) mixture in the PMB mixture (PMBM) posterior after each update step is approximated as a single MB. The efficient MB approximation is achieved by first running a blocked Gibbs sampler on the joint posterior of the set of trajectories and the measurement association variables. The single-object measurement model is assumed to be a Poisson point process which enables us to parallelize the sampling across all objects and association variables, respectively. Then, samples of object states are utilized to form the approximate MB density via Kullback-Leibler divergence minimization. Simulation results on EOT with known and constant elliptical shapes show that the TPMB implementation using blocked Gibbs sampling outperforms the state-of-the-art TPMB implementation using loopy belief propagation with significantly reduced runtime.
18.	Xia, Yuxuan, 1993, et al. (författare) Backward simulation for sets of trajectories 2020 Ingår i: Proceedings of 2020 23rd International Conference on Information Fusion, FUSION 2020. Konferensbidrag (refereegranskat)abstract This paper presents a solution for recovering full trajectory information, via the calculation of the posterior of the set of trajectories, from a sequence of multitarget (unlabelled) filtering densities and the multitarget dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multitarget filters that do not explicitly estimate trajectories. In this paper, we first derive a general multitrajectory forward-backward smoothing equation based on sets of trajectories and the random finite set framework. Then we show how to sample sets of trajectories using backward simulation when the multitarget filtering densities are multi-Bernoulli processes. The proposed approach is demonstrated in a simulation study.
19.	Xia, Yuxuan, 1993 (författare) Conjugate priors for Bayesian object tracking 2020 Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract Object tracking refers to the problem of using noisy sensor measurements to determine the location and characteristics of objects of interest in clutter. Nowadays, object tracking has found applications in numerous research venues as well as application areas, including air traffic control, maritime navigation, remote sensing, intelligent video surveillance, and more recently environmental perception, which is a key enabling technology in autonomous vehicles. This thesis studies conjugate priors for Bayesian object tracking with focus on multi-object tracking (MOT) based on sets of trajectories. Finite Set Statistics provides an elegant Bayesian formulation of MOT in terms of the theory of random finite sets (RFSs). Conjugate priors are also of great interest as they provide families of distributions that are suitable to work with when seeking accurate approximations to the true posterior distributions. Many RFS-based MOT approaches are only concerned with multi-object filtering without attempting to estimate object trajectories. An appealing approach to building tracks is by computing the multi-object densities on sets of trajectories. This leads to the development of trajectory filters, e.g., filters based on Poisson multi-Bernoulli mixture (PMBM) conjugate priors. In this thesis, [Paper A] and [Paper B] consider the problem of point object tracking where an object generates at most one measurement per scan. In [Paper A], it is shown that the trajectory MBM filter is the solution to the MOT problem for standard point object models with multi-Bernoulli birth. In addition, the multi-scan implementations of trajectory PMBM and MBM filters are presented. In [Paper B], a solution for recovering full trajectory information, via the calculation of the posterior of the set of trajectories from a sequence of multi-object filtering densities and the multi-object dynamic model, is presented. [Paper C] and [Paper D] consider the problem of ex- tended object tracking where an object may generate multiple measurements per scan. In [Paper C], the extended object PMBM filter for sets of objects is generalized to sets of trajectories. In [Paper D], a learning-based extended ob- ject tracking algorithm using a hierarchical truncated Gaussian measurement model tailored for automotive radar measurements is presented.
20.	Xia, Yuxuan, 1993, et al. (författare) Extended Object Tracking Using Hierarchical Truncation Measurement Model with Automotive Radar 2020 Ingår i: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 1520-6149. ; , s. 4900-4904 Konferensbidrag (refereegranskat)abstract Motivated by real-world automotive radar measurements that are distributed around object (e.g., vehicles) edges with a certain volume, a novel hierarchical truncated Gaussian measurement model is proposed to resemble the underlying spatial distribution of radar measurements. With the proposed measurement model, a modified random matrix-based extended object tracking algorithm is developed to estimate both kinematic and extent states. In particular, a new state update step and an online bound estimation step are proposed with the introduction of pseudo measurements. The effectiveness of the proposed algorithm is verified in simulations.
21.	Xia, Yuxuan, 1993, et al. (författare) Extended object tracking using hierarchical truncation model with partial-view measurements 2020 Ingår i: Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop. - 2151-870X. ; 2020 June Konferensbidrag (refereegranskat)abstract This paper introduces the hierarchical truncated Gaussian model in representing automotive radar measurements for extended object tracking. The model aims at a flexible spatial distribution with adaptive truncation bounds to account for partial-view measurements caused by self-occlusion. Built on a random matrix approach, we propose a new state update step together with an adaptively update of the truncation bounds. This is achieved by introducing spatial-domain pseudo measurements and by aggregating partial-view measurements over consecutive time-domain scans. The effectiveness of the proposed algorithm is verified on a synthetic dataset and an independent dataset generated using the MathWorks Automated Driving toolbox.
22.	Xia, Yuxuan, 1993, et al. (författare) Extended Object Tracking with Automotive Radar Using Learned Structural Measurement Model 2020 Ingår i: IEEE National Radar Conference - Proceedings. - 1097-5659. ; 2020-September Konferensbidrag (refereegranskat)abstract This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian with structural geometry parameters (e.g., truncation bounds, their orientation, and a scaling factor) learned from the training data. The contribution is twofold. First, the learned measurement model can provide an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Second, large-scale offline training datasets can be leveraged to learn the geometry-related parameters and offload the computationally demanding model parameter estimation from the state update step. The learned structural measurement model is further incorporated into the random matrix-based EOT approach with a new state update step. The effectiveness of the proposed approach is verified on the nuScenes dataset.
23.	Xia, Yuxuan, 1993, et al. (författare) Extended target Poisson multi-Bernoulli mixture trackers based on sets of trajectories 2019 Ingår i: FUSION 2019 - 22nd International Conference on Information Fusion. Konferensbidrag (refereegranskat)abstract The Poisson multi-Bernoulli mixture (PMBM) is a multi-target distribution for which the prediction and update are closed. By applying the random finite set (RFS) framework to multi-target tracking with sets of trajectories as the variable of interest, the PMBM trackers can efficiently estimate the set of target trajectories. This paper derives two trajectory RFS filters for extended target tracking, called extended target PMBM trackers. Compared to the extended target PMBM filter based on sets on targets, explicit track continuity between time steps is provided in the extended target PMBM trackers.
24.	Xia, Yuxuan, 1993, et al. (författare) Learning-Based Extended Object Tracking Using Hierarchical Truncation Measurement Model With Automotive Radar 2021 Ingår i: IEEE Journal on Selected Topics in Signal Processing. - 1941-0484 .- 1932-4553. ; 15:4, s. 1013-1029 Tidskriftsartikel (refereegranskat)abstract This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian (HTG) with structural geometry parameters that can be learned from the training data. The HTG measurement model provides an adequate resemblance to the spatial distribution of real-world automotive radar measurements. Moreover, large-scale radar datasets can be leveraged to learn the geometry-related model parameters and offload the computationally demanding model parameter estimation from the state update step. The learned HTG measurement model is further incorporated into a random matrix based EOT approach with two (multi-sensor) measurement updates: one is based on a factorized Gaussian inverse-Wishart density representation and the other is based on a Rao-Blackwellized particle density representation. The effectiveness of the proposed approaches is verified on both synthetic data and real-world nuScenes dataset over 300 trajectories.
25.	Xia, Yuxuan, 1993, et al. (författare) Multiple Object Trajectory Estimation Using Backward Simulation 2022 Ingår i: IEEE Transactions on Signal Processing. - 1941-0476 .- 1053-587X. ; 70, s. 3249-3263 Tidskriftsartikel (refereegranskat)abstract This paper presents a general solution for computing the multi-object posterior for sets of trajectories from a sequence of multi-object (unlabelled) filtering densities and a multi-object dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multi-object filters that do not explicitly estimate trajectories. In this paper, we first derive a general multi-trajectory backward smoothing equation based on random finite sets of trajectories. Then we show how to sample sets of trajectories using backward simulation for Poisson multi-Bernoulli filtering densities, and develop a tractable implementation based on ranked assignment. The performance of the resulting multi-trajectory particle smoothers is evaluated in a simulation study, and the results demonstrate that they have superior performance in comparison to several state-of-the-art multi-object filters and smoothers.
26.	Xia, Yuxuan, 1993, et al. (författare) Multiscan implementation of the trajectory poisson multi-Bernoulli mixture filter 2019 Ingår i: Journal of Advances in Information Fusion. - 1557-6418. ; 14:2, s. 213-235 Tidskriftsartikel (refereegranskat)abstract The Poisson multi-Bernoulli mixture (PMBM) and the multi-Bernoulli mixture (MBM) are two multitarget distributions for which closed-form filtering recursions exist. The PMBM has a Poisson birth process, whereas the MBM has a multi-Bernoulli birth process. This paper considers a recently developed formulation of the multitarget tracking problem using a random finite set of trajectories, through which the track continuity is explicitly established. A multiscan trajectory PMBM filter and a multiscan trajectory MBM filter, with the ability to correct past data association decisions to improve current decisions, are presented. In addition, a multiscan trajectory MBM01 filter, in which the existence probabilities of all Bernoulli components are either 0 or 1, is presented. This paper proposes an efficient implementation that performs track-oriented N-scan pruning to limit computational complexity, and uses dual decomposition to solve the involved multiframe assignment problem. The performance of the presented multitarget trackers, applied with an efficient fixed-lag smoothing method, is evaluated in a simulation study.
27.	Xia, Yuxuan, 1993, et al. (författare) Poisson Multi-Bernoulli Approximations for Multiple Extended Object Filtering 2022 Ingår i: IEEE Transactions on Aerospace and Electronic Systems. - 1557-9603 .- 0018-9251. ; 58:2, s. 890-906 Tidskriftsartikel (refereegranskat)
28.	Xia, Yuxuan, 1993 (författare) Poisson Multi-Bernoulli Mixtures for Multiple Object Tracking 2022 Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract Multi-object tracking (MOT) refers to the process of estimating object trajectories of interest based on sequences of noisy sensor measurements obtained from multiple sources. Nowadays, MOT has found applications in numerous areas, including, e.g., air traffic control, maritime navigation, remote sensing, intelligent video surveillance, and more recently environmental perception, which is a key enabling technology in automated vehicles. This thesis studies Poisson multi-Bernoulli mixture (PMBM) conjugate priors for MOT. Finite Set Statistics provides an elegant Bayesian formulation of MOT based on random finite sets (RFSs), and a significant trend in RFSs-based MOT is the development of conjugate distributions in Bayesian probability theory, such as the PMBM distributions. Multi-object conjugate priors are of great interest as they provide families of distributions that are suitable to work with when seeking accurate approximations to the true posterior distributions. Many RFS-based MOT approaches are only concerned with multi-object filtering without attempting to estimate object trajectories. An appealing approach to building trajectories is by computing the multi-object densities on sets of trajectories. This leads to the development of many multi-object filters based on sets of trajectories, e.g., the trajectory PMBM filters. In this thesis, [Paper A] and [Paper B] consider the problem of point object tracking where an object generates at most one measurement per time scan. In [Paper A], a multi-scan implementation of trajectory PMBM filters via dual decomposition is presented. In [Paper B], a multi-trajectory particle smoother using backward simulation is presented for computing the multi-object posterior for sets of trajectories using a sequence of multi-object filtering densities and a multi-object dynamic model. [Paper C] and [Paper D] consider the problem of extended object tracking where an object may generate multiple measurements per time scan. In [Paper C], an extended object Poisson multi-Bernoulli (PMB) filter is presented, where the PMBM posterior density after the update step is approximated as a PMB. In [Paper D], a trajectory PMB filter for extended object tracking using belief propagation is presented, where the efficient PMB approximation is enabled by leveraging the PMBM conjugacy and the factor graph formulation.
29.	Xia, Yuxuan, 1993, et al. (författare) Trajectory PMB Filters for Extended Object Tracking Using Belief Propagation 2023 Ingår i: IEEE Transactions on Aerospace and Electronic Systems. - 1557-9603 .- 0018-9251. ; 59:6, s. 9312-9331 Tidskriftsartikel (refereegranskat)abstract In this paper, we propose a Poisson multi-Bernoulli (PMB) filter for extended object tracking (EOT), which directly estimates the set of object trajectories, using belief propagation (BP). The proposed filter propagates a PMB density on the posterior of sets of trajectories through the filtering recursions over time, where the PMB mixture (PMBM) posterior after the update step is approximated as a PMB. The efficient PMB approximation relies on several important theoretical contributions. First, we present a PMBM conjugate prior on the posterior of sets of trajectories for a generalized measurement model, in which each object generates an independent set of measurements. The PMBM density is a conjugate prior in the sense that both the prediction and the update steps preserve the PMBM form of the density. Second, we present a factor graph representation of the joint posterior of the PMBM set of trajectories and association variables for the Poisson spatial measurement model. Importantly, leveraging the PMBM conjugacy and the factor graph formulation enables an elegant treatment on undetected objects via a Poisson point process and efficient inference on sets of trajectories using BP, where the approximate marginal densities in the PMB approximation can be obtained without enumeration of different data association hypotheses. To achieve this, we present a particle-based implementation of the proposed filter, where smoothed trajectory estimates, if desired, can be obtained via single-object particle smoothing methods, and its performance for EOT with ellipsoidal shapes is evaluated in a simulation study.
30.	Xiong, Weiyi, et al. (författare) Contrastive Learning for Automotive mmWave Radar Detection Points Based Instance Segmentation 2022 Ingår i: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. ; 2022-October, s. 1255-1261 Konferensbidrag (refereegranskat)abstract The automotive mmWave radar plays a key role in advanced driver assistance systems (ADAS) and autonomous driving. Deep learning-based instance segmentation enables real-time object identification from the radar detection points. In the conventional training process, accurate annotation is the key. However, high-quality annotations of radar detection points are challenging to achieve due to their ambiguity and sparsity. To address this issue, we propose a contrastive learning approach for implementing radar detection points-based instance segmentation. We define the positive and negative samples according to the ground-truth label, apply the contrastive loss to train the model first, and then perform fine-tuning for the following downstream task. In addition, these two steps can be merged into one, and pseudo labels can be generated for the unlabeled data to improve the performance further. Thus, there are four different training settings for our method. Experiments show that when the ground-truth information is only available for a small proportion of the training data, our method still achieves a comparable performance to the approach trained in a supervised manner with 100% ground-truth information.
31.	Xiong, Weiyi, et al. (författare) LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion 2024 Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 9:1, s. 79-92 Tidskriftsartikel (refereegranskat)abstract As an emerging technology and a relatively affordable device, the 4D imaging radar has already been confirmed effective in performing 3D object detection in autonomous driving. Nevertheless, the sparsity and noisiness of 4D radar point clouds hinder further performance improvement, and in-depth studies about its fusion with other modalities are lacking. On the other hand, as a new image view transformation strategy, “sampling” has been applied in a few image-based detectors and shown to outperform the widely applied “depth-based splatting” proposed in Lift-Splat-Shoot (LSS), even without image depth prediction. However, the potential of “sampling” is not fully unleashed. This paper investigates the “sampling” view transformation strategy on the camera and 4D imaging radar fusion-based 3D object detection. LiDAR Excluded Lean (LXL) model, predicted image depth distribution maps and radar 3D occupancy grids are generated from image perspective view (PV) features and radar bird's eye view (BEV) features, respectively. They are sent to the core of LXL, called “radar occupancy-assisted depth-based sampling”, to aid image view transformation. We demonstrated that more accurate view transformation can be performed by introducing image depths and radar information to enhance the “sampling” strategy. Experiments on VoD and TJ4DRadSet datasets show that the proposed method outperforms the state-of-the-art 3D object detection methods by a significant margin without bells and whistles. Ablation studies demonstrate that our method performs the best among different enhancement settings.

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