| 1. |
- Brissman, Emil, 1987-
(författare)
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Learning to Analyze Visual Data Streams for Environment Perception
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A mobile robot, instructed by a human operator, acts in an environment with many other objects. However, for an autonomous robot, human instructions should be minimal and only high-level instructions, such as the ultimate task or destination. In order to increase the level of autonomy, it has become a foremost objective to mimic human vision using neural networks that take a stream of images as input and learn a specific computer vision task from large amounts of data. In this thesis, we explore several different models for surround sensing, each of which contributes to a higher understanding of the environment being possible. As its first contribution, this thesis presents an object tracking method for video sequences, which is a crucial component in a perception system. This method predicts a fine-grained mask to separate the pixels corresponding to the target from those corresponding to the background. Rather than tracking location and size, the method tracks the initial pixels assigned to the target in this so-called video object segmentation. For subsequent time steps, the goal is to learn how the target looks using features from a neural network. We named our method A-GAME, based on the generative modeling of deep feature space, separating target and background appearances. In the second contribution of this thesis, we detect, track, and segment all objects from a set of predefined object classes. This information is how the robot increases its capabilities to perceive the surroundings. We experiment with a graph neural network to weigh all new detections and existing tracks. This model outperforms prior works by separating visually, and semantically similar objects frame by frame. The third contribution investigates one limitation of anchor-based detectors, which classify pre-defined bounding boxes as either negative or positive and thus provide a limited set of handled object shapes. One idea is to learn an alternative instance representation. We experiment with a neural network that predicts the distance to the nearest object contour in different directions from each pixel. The network then computes an approximated signed distance function containing the respective instance information. Last, this thesis studies a concept within model validation. We observed that overfitting could increase performance on benchmarks. However, this opportunity is insipid for sensing systems in practice since measurements, such as length or angles, are quantities that explain the environment. The fourth contribution of this thesis is an extended validation technique for camera calibration. This technique uses a statistical model for each error difference between an observed value and a corresponding prediction of the projective model. We compute a test over the differences and detect if the projective model is incorrect.
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| 2. |
- Kristan, Matej, et al.
(författare)
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The Ninth Visual Object Tracking VOT2021 Challenge Results
- 2021
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Ingår i: 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2021). - : IEEE COMPUTER SOC. - 9781665401913 ; , s. 2711-2738
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, (ii) VOT-RT2021 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2021 focused on long-term tracking, namely coping with target disappearance and reappearance and (iv) VOT-RGBD2021 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2021 dataset was refreshed, while VOT-RGBD2021 introduces a training dataset and sequestered dataset for winner identification. The source code for most of the trackers, the datasets, the evaluation kit and the results along with the source code for most trackers are publicly available at the challenge website(1).
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| 3. |
- Larsson, Viktor, et al.
(författare)
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Uncovering symmetries in polynomial systems
- 2016
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Ingår i: Computer Vision – ECCV 2016 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783319464862 - 9783319464879 ; 9907, s. 252-267
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Konferensbidrag (refereegranskat)abstract
- In this paper we study symmetries in polynomial equation systems and how they can be integrated into the action matrix method. The main contribution is a generalization of the partial p-fold symmetry and we provide new theoretical insights as to why these methods work. We show several examples of how to use this symmetry to construct more compact polynomial solvers. As a second contribution we present a simple and automatic method for finding these symmetries for a given problem. Finally we show two examples where these symmetries occur in real applications.
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| 4. |
- Mosberger, Rafael, 1980-, et al.
(författare)
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Multi-band Hough Forests for detecting humans with Reflective Safety Clothing from mobile machinery
- 2015
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Ingår i: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). - : IEEE Computer Society. - 9781479969234 ; , s. 697-703
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Konferensbidrag (refereegranskat)abstract
- We address the problem of human detection from heavy mobile machinery and robotic equipment operating at industrial working sites. Exploiting the fact that workers are typically obliged to wear high-visibility clothing with reflective markers, we propose a new recognition algorithm that specifically incorporates the highly discriminative features of the safety garments in the detection process. Termed Multi-band Hough Forest, our detector fuses the input from active near-infrared (NIR) and RGB color vision to learn a human appearance model that not only allows us to detect and localize industrial workers, but also to estimate their body orientation. We further propose an efficient pipeline for automated generation of training data with high-quality body part annotations that are used in training to increase detector performance. We report a thorough experimental evaluation on challenging image sequences from a real-world production environment, where persons appear in a variety of upright and non-upright body positions.
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| 5. |
- Triebel, Rudolph, et al.
(författare)
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SPENCER : A Socially Aware Service Robot for Passenger Guidance and Help in Busy Airports
- 2016
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Ingår i: Field and Service Robotics. - Cham : Springer. - 9783319277028 - 9783319277004 ; , s. 607-622
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Konferensbidrag (refereegranskat)abstract
- We present an ample description of a socially compliant mobile robotic platform, which is developed in the EU-funded project SPENCER. The purpose of this robot is to assist, inform and guide passengers in large and busy airports. One particular aim is to bring travellers of connecting flights conveniently and efficiently from their arrival gate to the passport control. The uniqueness of the project stems from the strong demand of service robots for this application with a large potential impact for the aviation industry on one side, and on the other side from the scientific advancements in social robotics, brought forward and achieved in SPENCER. The main contributions of SPENCER are novel methods to perceive, learn, and model human social behavior and to use this knowledge to plan appropriate actions in real-time for mobile platforms. In this paper, we describe how the project advances the fields of detection and tracking of individuals and groups, recognition of human social relations and activities, normative human behavior learning, socially-aware task and motion planning, learning socially annotated maps, and conducting empirical experiments to assess socio-psychological effects of normative robot behaviors.
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