CoopTrack: Exploring End-to-End Learning for Efficient Cooperative Sequential Perception

• URL: http://arxiv.org/abs/2507.19239v1
• Date: Fri, 25 Jul 2025 13:04:54 GMT
• Title: CoopTrack: Exploring End-to-End Learning for Efficient Cooperative Sequential Perception
• Authors: Jiaru Zhong, Jiahao Wang, Jiahui Xu, Xiaofan Li, Zaiqing Nie, Haibao Yu,
• Abstract summary: We propose CoopTrack, a fully instance-level end-to-end framework for cooperative tracking.<n>CoopTrack features learnable instance association, which fundamentally differs from existing approaches.<n>Experiments on both the V2X-Seq and Griffin datasets demonstrate that CoopTrack achieves excellent performance.
• Score: 13.32869419720427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cooperative perception aims to address the inherent limitations of single-vehicle autonomous driving systems through information exchange among multiple agents. Previous research has primarily focused on single-frame perception tasks. However, the more challenging cooperative sequential perception tasks, such as cooperative 3D multi-object tracking, have not been thoroughly investigated. Therefore, we propose CoopTrack, a fully instance-level end-to-end framework for cooperative tracking, featuring learnable instance association, which fundamentally differs from existing approaches. CoopTrack transmits sparse instance-level features that significantly enhance perception capabilities while maintaining low transmission costs. Furthermore, the framework comprises two key components: Multi-Dimensional Feature Extraction, and Cross-Agent Association and Aggregation, which collectively enable comprehensive instance representation with semantic and motion features, and adaptive cross-agent association and fusion based on a feature graph. Experiments on both the V2X-Seq and Griffin datasets demonstrate that CoopTrack achieves excellent performance. Specifically, it attains state-of-the-art results on V2X-Seq, with 39.0\% mAP and 32.8\% AMOTA. The project is available at https://github.com/zhongjiaru/CoopTrack.

Related papers

• What Makes Good Collaborative Views? Contrastive Mutual Information Maximization for Multi-Agent Perception [52.41695608928129]
  Multi-agent perception (MAP) allows autonomous systems to understand complex environments by interpreting data from multiple sources. This paper investigates intermediate collaboration for MAP with a specific focus on exploring "good" properties of collaborative view. We propose a novel framework named CMiMC for intermediate collaboration.
  arXiv  Detail & Related papers  (2024-03-15T07:18:55Z)
• V2X-AHD:Vehicle-to-Everything Cooperation Perception via Asymmetric Heterogenous Distillation Network [13.248981195106069]
  We propose a multi-view vehicle-road cooperation perception system, vehicle-to-everything cooperative perception (V2X-AHD) The V2X-AHD can effectively improve the accuracy of 3D object detection and reduce the number of network parameters, according to this study.
  arXiv  Detail & Related papers  (2023-10-10T13:12:03Z)
• M$^3$Net: Multi-view Encoding, Matching, and Fusion for Few-shot Fine-grained Action Recognition [80.21796574234287]
  M$3$Net is a matching-based framework for few-shot fine-grained (FS-FG) action recognition. It incorporates textitmulti-view encoding, textitmulti-view matching, and textitmulti-view fusion to facilitate embedding encoding, similarity matching, and decision making. Explainable visualizations and experimental results demonstrate the superiority of M$3$Net in capturing fine-grained action details.
  arXiv  Detail & Related papers  (2023-08-06T09:15:14Z)
• Practical Collaborative Perception: A Framework for Asynchronous and Multi-Agent 3D Object Detection [9.967263440745432]
  Occlusion is a major challenge for LiDAR-based object detection methods. State-of-the-art V2X methods resolve the performance-bandwidth tradeoff using a mid-collaboration approach. We devise a simple yet effective collaboration method that achieves a better bandwidth-performance tradeoff than prior methods.
  arXiv  Detail & Related papers  (2023-07-04T03:49:42Z)
• A Dynamic Feature Interaction Framework for Multi-task Visual Perception [100.98434079696268]
  We devise an efficient unified framework to solve multiple common perception tasks. These tasks include instance segmentation, semantic segmentation, monocular 3D detection, and depth estimation. Our proposed framework, termed D2BNet, demonstrates a unique approach to parameter-efficient predictions for multi-task perception.
  arXiv  Detail & Related papers  (2023-06-08T09:24:46Z)
• VINet: Lightweight, Scalable, and Heterogeneous Cooperative Perception for 3D Object Detection [15.195933965761645]
  Cooperative Perception (CP) has emerged to significantly advance the perception of automated driving. We introduce VINet, a unified deep learning-based CP network for scalable, lightweight, and heterogeneous cooperative 3D object detection. VINet can reduce 84% system-level computational cost and 94% system-level communication cost while improving the 3D detection accuracy.
  arXiv  Detail & Related papers  (2022-12-14T07:03:23Z)
• A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection [59.21990697929617]
  Humans tend to mine objects by learning from a group of images or several frames of video since we live in a dynamic world. Previous approaches design different networks on similar tasks separately, and they are difficult to apply to each other. We introduce a unified framework to tackle these issues, term as UFO (UnifiedObject Framework for Co-Object Framework)
  arXiv  Detail & Related papers  (2022-03-09T13:35:19Z)
• CompFeat: Comprehensive Feature Aggregation for Video Instance Segmentation [67.17625278621134]
  Video instance segmentation is a complex task in which we need to detect, segment, and track each object for any given video. Previous approaches only utilize single-frame features for the detection, segmentation, and tracking of objects. We propose a novel comprehensive feature aggregation approach (CompFeat) to refine features at both frame-level and object-level with temporal and spatial context information.
  arXiv  Detail & Related papers  (2020-12-07T00:31:42Z)
• LiDAR-based Panoptic Segmentation via Dynamic Shifting Network [56.71765153629892]
  LiDAR-based panoptic segmentation aims to parse both objects and scenes in a unified manner. We propose the Dynamic Shifting Network (DS-Net), which serves as an effective panoptic segmentation framework in the point cloud realm. Our proposed DS-Net achieves superior accuracies over current state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-11-24T08:44:46Z)
• End-to-End 3D Multi-Object Tracking and Trajectory Forecasting [34.68114553744956]
  We propose a unified solution for 3D MOT and trajectory forecasting. We employ a feature interaction technique by introducing Graph Neural Networks. We also use a diversity sampling function to improve the quality and diversity of our forecasted trajectories.
  arXiv  Detail & Related papers  (2020-08-25T16:54:46Z)
• Cascaded Human-Object Interaction Recognition [175.60439054047043]
  We introduce a cascade architecture for a multi-stage, coarse-to-fine HOI understanding. At each stage, an instance localization network progressively refines HOI proposals and feeds them into an interaction recognition network. With our carefully-designed human-centric relation features, these two modules work collaboratively towards effective interaction understanding.
  arXiv  Detail & Related papers  (2020-03-09T17:05:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.