| 1. |
- Berg, Amanda, 1988-, et al.
(författare)
-
An Overview of the Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge
- 2016
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The Thermal Infrared Visual Object Tracking (VOT-TIR2015) Challenge was organized in conjunction with ICCV2015. It was the first benchmark on short-term,single-target tracking in thermal infrared (TIR) sequences. The challenge aimed at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. It was based on the VOT2013 Challenge, but introduced the following novelties: (i) the utilization of the LTIR (Linköping TIR) dataset, (ii) adaption of the VOT2013 attributes to thermal data, (iii) a similar evaluation to that of VOT2015. This paper provides an overview of the VOT-TIR2015 Challenge as well as the results of the 24 participating trackers.
|
|
| 2. |
- Danelljan, Martin, 1989-, et al.
(författare)
-
Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking
- 2016
-
Ingår i: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781467388511 - 9781467388528 ; , s. 1430-1438
-
Konferensbidrag (refereegranskat)abstract
- Tracking-by-detection methods have demonstrated competitive performance in recent years. In these approaches, the tracking model heavily relies on the quality of the training set. Due to the limited amount of labeled training data, additional samples need to be extracted and labeled by the tracker itself. This often leads to the inclusion of corrupted training samples, due to occlusions, misalignments and other perturbations. Existing tracking-by-detection methods either ignore this problem, or employ a separate component for managing the training set. We propose a novel generic approach for alleviating the problem of corrupted training samples in tracking-by-detection frameworks. Our approach dynamically manages the training set by estimating the quality of the samples. Contrary to existing approaches, we propose a unified formulation by minimizing a single loss over both the target appearance model and the sample quality weights. The joint formulation enables corrupted samples to be down-weighted while increasing the impact of correct ones. Experiments are performed on three benchmarks: OTB-2015 with 100 videos, VOT-2015 with 60 videos, and Temple-Color with 128 videos. On the OTB-2015, our unified formulation significantly improves the baseline, with a gain of 3.8% in mean overlap precision. Finally, our method achieves state-of-the-art results on all three datasets.
|
|
| 3. |
- Danelljan, Martin, 1989-, et al.
(författare)
-
Discriminative Scale Space Tracking
- 2017
-
Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE COMPUTER SOC. - 0162-8828 .- 1939-3539. ; 39:8, s. 1561-1575
-
Tidskriftsartikel (refereegranskat)abstract
- Accurate scale estimation of a target is a challenging research problem in visual object tracking. Most state-of-the-art methods employ an exhaustive scale search to estimate the target size. The exhaustive search strategy is computationally expensive and struggles when encountered with large scale variations. This paper investigates the problem of accurate and robust scale estimation in a tracking-by-detection framework. We propose a novel scale adaptive tracking approach by learning separate discriminative correlation filters for translation and scale estimation. The explicit scale filter is learned online using the target appearance sampled at a set of different scales. Contrary to standard approaches, our method directly learns the appearance change induced by variations in the target scale. Additionally, we investigate strategies to reduce the computational cost of our approach. Extensive experiments are performed on the OTB and the VOT2014 datasets. Compared to the standard exhaustive scale search, our approach achieves a gain of 2.5 percent in average overlap precision on the OTB dataset. Additionally, our method is computationally efficient, operating at a 50 percent higher frame rate compared to the exhaustive scale search. Our method obtains the top rank in performance by outperforming 19 state-of-the-art trackers on OTB and 37 state-of-the-art trackers on VOT2014.
|
|
| 4. |
- Felsberg, Michael, et al.
(författare)
-
The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results
- 2015
-
Ingår i: Proceedings of the IEEE International Conference on Computer Vision. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781467383905 ; , s. 639-651
-
Konferensbidrag (refereegranskat)abstract
- The Thermal Infrared Visual Object Tracking challenge 2015, VOTTIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply prelearned models of object appearance. VOT-TIR2015 is the first benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Linköping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015.
|
|
| 5. |
- Felsberg, Michael, 1974-, et al.
(författare)
-
The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results
- 2016
-
Ingår i: Computer Vision – ECCV 2016 Workshops. ECCV 2016.. - Cham : SPRINGER INT PUBLISHING AG. - 9783319488813 - 9783319488806 ; , s. 824-849
-
Konferensbidrag (refereegranskat)abstract
- The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.
|
|
| 6. |
- Häger, Gustav, 1988-, et al.
(författare)
-
Combining Visual Tracking and Person Detection for Long Term Tracking on a UAV
- 2016
-
Ingår i: Proceedings of the 12th International Symposium on Advances in Visual Computing. - Cham : Springer. - 9783319508344 - 9783319508351
-
Konferensbidrag (refereegranskat)abstract
- Visual object tracking performance has improved significantly in recent years. Most trackers are based on either of two paradigms: online learning of an appearance model or the use of a pre-trained object detector. Methods based on online learning provide high accuracy, but are prone to model drift. The model drift occurs when the tracker fails to correctly estimate the tracked object’s position. Methods based on a detector on the other hand typically have good long-term robustness, but reduced accuracy compared to online methods.Despite the complementarity of the aforementioned approaches, the problem of fusing them into a single framework is largely unexplored. In this paper, we propose a novel fusion between an online tracker and a pre-trained detector for tracking humans from a UAV. The system operates at real-time on a UAV platform. In addition we present a novel dataset for long-term tracking in a UAV setting, that includes scenarios that are typically not well represented in standard visual tracking datasets.
|
|
| 7. |
- Häger, Gustav, 1988-, et al.
(författare)
-
Countering bias in tracking evaluations
- 2018
-
Ingår i: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. - : Science and Technology Publications, Lda. - 9789897582905 ; , s. 581-587
-
Konferensbidrag (refereegranskat)abstract
- Recent years have witnessed a significant leap in visual object tracking performance mainly due to powerfulfeatures, sophisticated learning methods and the introduction of benchmark datasets. Despite this significantimprovement, the evaluation of state-of-the-art object trackers still relies on the classical intersection overunion (IoU) score. In this work, we argue that the object tracking evaluations based on classical IoU score aresub-optimal. As our first contribution, we theoretically prove that the IoU score is biased in the case of largetarget objects and favors over-estimated target prediction sizes. As our second contribution, we propose a newscore that is unbiased with respect to target prediction size. We systematically evaluate our proposed approachon benchmark tracking data with variations in relative target size. Our empirical results clearly suggest thatthe proposed score is unbiased in general.
|
|
| 8. |
- Häger, Gustav, 1988-
(författare)
-
Learning visual perception for autonomous systems
- 2021
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In the last decade, developments in hardware, sensors and software have made it possible to create increasingly autonomous systems. These systems can be as simple as limited driver assistance software lane-following in cars, or limited collision warning systems for otherwise manually piloted drones. On the other end of the spectrum exist fully autonomous cars, boats or helicopters. With increasing abilities to function autonomously, the demands to operate with minimal human supervision in unstructured environments increase accordingly.Common to most, if not all, autonomous systems is that they require an accurate model of the surrounding world. While there is currently a large number of possible sensors useful to create such models available, cameras are one of the most versatile. From a sensing perspective cameras have several advantages over other sensors in that they require no external infrastructure, are relatively cheap and can be used to extract such information as the relative positions of other objects, their movements over time, create accurate maps and locate the autonomous system within these maps.Using cameras to produce a model of the surroundings require solving a number of technical problems. Often these problems have a basis in recognizing that an object or region of interest is the same over time or in novel viewpoints. In visual tracking this type of recognition is required to follow an object of interest through a sequence of images. In geometric problems it is often a requirement to recognize corresponding image regions in order to perform 3D reconstruction or localization. The first set of contributions in this thesis is related to the improvement of a class of on-line learned visual object trackers based on discriminative correlation filters. In visual tracking estimation of the objects size is important for reliable tracking, the first contribution in this part of the thesis investigates this problem. The performance of discriminative correlation filters is highly dependent on what feature representation is used by the filter. The second tracking contribution investigates the performance impact of different features derived from a deep neural network.A second set of contributions relate to the evaluation of visual object trackers. The first of these are the visual object tracking challenge. This challenge is a yearly comparison of state-of-the art visual tracking algorithms. A second contribution is an investigation into the possible issues when using bounding-box representations for ground-truth data.In real world settings tracking typically occur over longer time sequences than is common in benchmarking datasets. In such settings it is common that the model updates of many tracking algorithms cause the tracker to fail silently. For this reason it is important to have an estimate of the trackers performance even in cases when no ground-truth annotations exist. The first of the final three contributions investigates this problem in a robotics setting, by fusing information from a pre-trained object detector in a state-estimation framework. An additional contribution describes how to dynamically re-weight the data used for the appearance model of a tracker. A final contribution investigates how to obtain an estimate of how certain detections are in a setting where geometrical limitations can be imposed on the search region. The proposed solution learns to accurately predict stereo disparities along with accurate assessments of each predictions certainty.
|
|
| 9. |
- Häger, Gustav, 1988-, et al.
(författare)
-
Predicting Disparity Distributions
- 2021
-
Ingår i: 2021 IEEE International Conference on Robotics and Automation (ICRA). - : IEEE. - 9781728190778 - 9781728190785
-
Konferensbidrag (refereegranskat)abstract
- We investigate a novel deep-learning-based approach to estimate uncertainty in stereo disparity prediction networks. Current state-of-the-art methods often formulate disparity prediction as a regression problem with a single scalar output in each pixel. This can be problematic in practical applications as in many cases there might not exist a single well defined disparity, for example in cases of occlusions or at depth-boundaries. While current neural-network-based disparity estimation approaches obtain good performance on benchmarks, the disparity prediction is treated as a black box at inference time. In this paper we show that by formulating the learning problem as a regression with a distribution target, we obtain a robust estimate of the uncertainty in each pixel, while maintaining the performance of the original method. The proposed method is evaluated both on a large-scale standard benchmark, as well on our own data. We also show that the uncertainty estimate significantly improves by maximizing the uncertainty in those pixels that have no well defined disparity during learning.
|
|
| 10. |
- Persson, Mikael, 1985-, et al.
(författare)
-
Practical Pose Trajectory Splines With Explicit Regularization
- 2021
-
Ingår i: 2021 INTERNATIONAL CONFERENCE ON 3D VISION (3DV 2021). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665426886 - 9781665426893 ; , s. 156-165
-
Konferensbidrag (refereegranskat)abstract
- We investigate spline-based continuous-time pose trajectory estimation using non-linear explicit motion priors. Current regularization priors either linearize the orientation, rely on the implicit regularization obtained from the used spline basis function, or use sampling based regularization schemes. The latter is a special case of a Riemann sum approximation, and we demonstrate when and why this can fail, and propose a way to avoid these issues. In addition we provide a number of novel practically useful theoretical contributions, including requirements on knot spacing for orientation splines, new basis functions for constant velocity extrapolation, and a generalization of the popular P-Spline penalty to orientation. We analyze the properties of the proposed approach using synthetic data. We validate our system using the standard task of visual-inertial calibration, and apply it to stereo visual odometry where we demonstrate real-time performance on KITTI.
|
|
