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17th European Conference on Computer Vision, ECCV 2022

作者： Bansal, Vaibhav Micheloni, Christian Foresti, Gianluca Martinel, Niki Machine Learning and Perception Lab University of Udine Udine Italy

ISBN: (纸本)9783031250743

In the recent past, the focus of the research community in the field of person re-identification (ReID) has gradually shifted towards video-based ReID where the goal is to identify and associate specific person identities from videos captured by different cameras at different times. A key challenge is to effectively model spatial and temporal information for robust and discrimintative video feature representation. Another challenge arises from the assumption that the clothing of the target persons would remain consistent over long periods of time and thus, most of the existing methods rely on clothing appearance for re-identification. Such assumptions lead to errors in practical scenarios where clothing consistency does not hold true. An additional challenge comes in the form of limitations faced by existing methods that largely employ CNN-based networks since CNNs can only exploit local dependencies and lose significant information due to downsampling operations employed. To overcome all these challenges, we propose a Vision-transformer-based framework exploring space-time self-attention to address the problem of long-term cloth-changing ReID in videos (CCVID-ReID). For more unique discriminative representation, we believe that soft-biometric information such as gait features can be paired with the video features from the transformer-based framework. For getting such rich dynamic information, we use an existing state-of-the-art model for 3D motion estimation, VIBE. To provide compelling evidence in favour of our approach of utilizing spatio-temporal information to address CCVID-ReID, we evaluate our method on a variant of recently published long-term cloth-changing ReID dataset, PRCC. The experiments demonstrate the proposed approach achieves state-of-the-art results which, we believe, will invite further focus in this direction. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Motion estimation

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Visualizing Skiers' Trajectories in Monocular Videos

Visualizing Skiers' Trajectories in Monocular Videos

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Dunnhofer, Matteo Sordi, Luca Micheloni, Christian University of Udine Machine Learning and Perception Lab Udine Italy

ISBN: (纸本)9798350302493

Trajectories are fundamental to winning in alpine skiing. Tools enabling the analysis of such curves can enhance the training activity and enrich broadcasting content. In this paper, we propose SkiTraVis, an algorithm to visualize the sequence of points traversed by a skier during its performance. SkiTraVis works on monocular videos and constitutes a pipeline of a visual tracker to model the skier's motion and of a frame correspondence module to estimate the camera's motion. The separation of the two motions enables the visualization of the trajectory according to the moving camera's perspective. We performed experiments on videos of real-world professional competitions to quantify the visualization error, the computational efficiency, as well as the applicability. Overall, the results achieved demonstrate the potential of our solution for broadcasting media enhancement and coach assistance. © 2023 IEEE.

关键词： Trajectories

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learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios With Multiagent Reinforcement learning

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IEEE Systems Journal 2025年第1期19卷 317-326页

作者： Liu, Weiwei Hu, Wenxuan Jing, Wei Lei, Lanxin Gao, Lingping Liu, Yong Zhejiang University The Advanced Perception on Robotics and Intelligent Learning Lab College of Control Science and Engineering Hangzhou310027 China Huzhou Institute of Zhejiang University Zhejiang 310027 China Alibaba DAMO Academy Autonomous Driving Lab Zhejiang 311121 China Huzhou University College of Information Engineering Zhejiang 313000 China

Autonomous vehicles trained through multiagent reinforcement learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained policies can be impacted when faced with diverse driving styles and personalities, particularly in highly interactive situations. This is because conventional MARL algorithms usually operate under the assumption of fully cooperative behavior among all agents and focus on maximizing team rewards during training. To address this issue, we introduce the personality modeling network (PeMN), which includes a cooperation value function and personality parameters to model the varied interactions in high-interactive scenarios. The PeMN also enables the training of a background traffic flow with diverse behaviors, thereby improving the performance and generalization of the ego vehicle. Our extensive experimental studies, which incorporate different personality parameters in high-interactive driving scenarios, demonstrate that the personality parameters effectively model diverse driving styles and that policies trained with PeMN demonstrate better generalization than traditional MARL methods. © 2007-2012 IEEE.

关键词： Reinforcement learning

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Tracking Skiers from the Top to the Bottom

Tracking Skiers from the Top to the Bottom

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Matteo Dunnhofer Luca Sordi Niki Martinel Christian Micheloni Machine Learning and Perception Lab University of Udine Udine Italy

Skiing is a popular winter sport discipline with a long history of competitive events. In this domain, computer vision has the potential to enhance the understanding of athletes’ performance, but its application lags behind other sports due to limited studies and datasets. This paper makes a step forward in filling such gaps. A thorough investigation is performed on the task of skier tracking in a video capturing his/her complete performance. Obtaining continuous and accurate skier localization is preemptive for further higher-level performance analyses. To enable the study, the largest and most annotated dataset for computer vision in skiing, SkiTB, is introduced. Several visual object tracking algorithms, including both established methodologies and a newly introduced skier-optimized baseline algorithm, are tested using the dataset. The results provide valuable insights into the applicability of different tracking methods for vision-based skiing analysis. SkiTB, code, and results are available at https://***/datasets/skitb.

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Tracking Skiers from the Top to the Bottom

arXiv

引用

arXiv 2023年

作者： Dunnhofer, Matteo Sordi, Luca Martinel, Niki Micheloni, Christian Machine Learning and Perception Lab University of Udine Udine Italy

Skiing is a popular winter sport discipline with a long history of competitive events. In this domain, computer vision has the potential to enhance the understanding of athletes' performance, but its application lags behind other sports due to limited studies and datasets. This paper makes a step forward in filling such gaps. A thorough investigation is performed on the task of skier tracking in a video capturing his/her complete performance. Obtaining continuous and accurate skier localization is preemptive for further higher-level performance analyses. To enable the study, the largest and most annotated dataset for computer vision in skiing, SkiTB, is introduced. Several visual object tracking algorithms, including both established methodologies and a newly introduced skier-optimized baseline algorithm, are tested using the dataset. The results provide valuable insights into the applicability of different tracking methods for vision-based skiing analysis. SkiTB, code, and results are available at https://***/datasets/skitb. © 2023, CC BY.

关键词： Sports

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Visualizing Skiers' Trajectories in Monocular Videos

Visualizing Skiers' Trajectories in Monocular Videos

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Matteo Dunnhofer Luca Sordi Christian Micheloni Machine Learning and Perception Lab University of Udine Udine Italy

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Visualizing Skiers’ Trajectories in Monocular Videos

arXiv

引用

arXiv 2023年

作者： Dunnhofer, Matteo Sordi, Luca Micheloni, Christian Machine Learning and Perception Lab University of Udine Udine Italy

Trajectories are fundamental to winning in alpine skiing. Tools enabling the analysis of such curves can enhance the training activity and enrich broadcasting content. In this paper, we propose SkiTraVis, an algorithm to visualize the sequence of points traversed by a skier during its performance. SkiTraVis works on monocular videos and constitutes a pipeline of a visual tracker to model the skier’s motion and of a frame correspondence module to estimate the camera’s motion. The separation of the two motions enables the visualization of the trajectory according to the moving camera’s perspective. We performed experiments on videos of real-world professional competitions to quantify the visualization error, the computational efficiency, as well as the applicability. Overall, the results achieved demonstrate the potential of our solution for broadcasting media enhancement and coach assistance. © 2023, CC BY.

关键词： Trajectories

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Tracking-by-Trackers with a Distilled and Reinforced Model 15th

Tracking-by-Trackers with a Distilled and Reinforced Model

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15th Asian Conference on Computer Vision, ACCV 2020

作者： Dunnhofer, Matteo Martinel, Niki Micheloni, Christian Machine Learning and Perception Lab University of Udine Udine Italy

ISBN: (纸本)9783030695316

Visual object tracking was generally tackled by reasoning independently on fast processing algorithms, accurate online adaptation methods, and fusion of trackers. In this paper, we unify such goals by proposing a novel tracking methodology that takes advantage of other visual trackers, offline and online. A compact student model is trained via the marriage of knowledge distillation and reinforcement learning. The first allows to transfer and compress tracking knowledge of other trackers. The second enables the learning of evaluation measures which are then exploited online. After learning, the student can be ultimately used to build (i) a very fast single-shot tracker, (ii) a tracker with a simple and effective online adaptation mechanism, (iii) a tracker that performs fusion of other trackers. Extensive validation shows that the proposed algorithms compete with real-time state-of-the-art trackers. © 2021, Springer Nature Switzerland AG.

关键词： Distillation

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FunGraph: Functionality Aware 3D Scene Graphs for Language-Prompted Scene Interaction

arXiv

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arXiv 2025年

作者： Rotondi, Dennis Scaparro, Fabio Blum, Hermann Arras, Kai O. Socially Intelligent Robotics Lab Institute for Artificial Intelligence University of Stuttgart Germany Robot Perception and Learning Lab LAMARR Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

The concept of 3D scene graphs is increasingly recognized as a powerful semantic and hierarchical representation of the environment. Current approaches often address this at a coarse, object-level resolution. In contrast, our goal is to develop a representation that enables robots to directly interact with their environment by identifying both the location of functional interactive elements and how these can be used. To achieve this, we focus on detecting and storing objects at a finer resolution, focusing on affordance-relevant parts. The primary challenge lies in the scarcity of data that extends beyond instance-level detection and the inherent difficulty of capturing detailed object features using robotic sensors. We leverage currently available 3D resources to generate 2D data and train a detector, which is then used to augment the standard 3D scene graph generation pipeline. Through our experiments, we demonstrate that our approach achieves functional element segmentation comparable to state-of-the-art 3D models and that our augmentation enables task-driven affordance grounding with higher accuracy than the current solutions. © 2025, CC BY-NC-ND.

关键词： Semantics

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Guided Decoding for Robot On-line Motion Generation and Adaption 23

Guided Decoding for Robot On-line Motion Generation and Adap...

引用

23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Chen, Nutan Cseke, Botond Aljalbout, Elie Paraschos, Alexandros Alles, Marvin Van Der Smagt, Patrick Machine Learning Research Lab Volkswagen Group Germany Robotics and Perception Group Department of Informatics Switzerland Uzh Eth Zurich Department of Neuroinformatics Switzerland Eötvös Loránd University Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350373578

We present a novel motion generation approach for robot arms, with high degrees of freedom, in complex settings that can adapt online to obstacles or new via points. learning from Demonstration facilitates rapid adaptation to new tasks and optimizes the utilization of accumulated expertise by allowing robots to learn and generalize from demonstrated trajectories. We train a transformer architecture, based on conditional variational autoencoder, on a large dataset of simulated trajectories used as demonstrations. Our architecture learns essential motion generation skills from these demonstrations and is able to adapt them to meet auxiliary tasks. Additionally, our approach implements auto-regressive motion generation to enable real-time adaptations, as, for example, introducing or changing via-points, and velocity and acceleration constraints. Using beam search, we present a method for further adaption of our motion generator to avoid obstacles. We show that our model successfully generates motion from different initial and target points and that is capable of generating trajectories that navigate complex tasks across different robotic platforms. © 2024 IEEE.

关键词： Trajectories

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