Visible-Thermal Multiple Object Tracking: Large-scale Video Dataset and Progressive Fusion Approach

• URL: http://arxiv.org/abs/2408.00969v1
• Date: Fri, 2 Aug 2024 01:29:43 GMT
• Title: Visible-Thermal Multiple Object Tracking: Large-scale Video Dataset and Progressive Fusion Approach
• Authors: Yabin Zhu, Qianwu Wang, Chenglong Li, Jin Tang, Zhixiang Huang,
• Abstract summary: We contribute a large-scale Visible-Thermal video benchmark for Multiple Object Tracking (MOT) called VT-MOT. VT-MOT includes 582 video sequence pairs, 401k frame pairs from surveillance, drone, and handheld platforms. A comprehensive experiment are conducted on VT-MOT and the results prove the superiority and effectiveness of the proposed method.
• Score: 17.286142856787222
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The complementary benefits from visible and thermal infrared data are widely utilized in various computer vision task, such as visual tracking, semantic segmentation and object detection, but rarely explored in Multiple Object Tracking (MOT). In this work, we contribute a large-scale Visible-Thermal video benchmark for MOT, called VT-MOT. VT-MOT has the following main advantages. 1) The data is large scale and high diversity. VT-MOT includes 582 video sequence pairs, 401k frame pairs from surveillance, drone, and handheld platforms. 2) The cross-modal alignment is highly accurate. We invite several professionals to perform both spatial and temporal alignment frame by frame. 3) The annotation is dense and high-quality. VT-MOT has 3.99 million annotation boxes annotated and double-checked by professionals, including heavy occlusion and object re-acquisition (object disappear and reappear) challenges. To provide a strong baseline, we design a simple yet effective tracking framework, which effectively fuses temporal information and complementary information of two modalities in a progressive manner, for robust visible-thermal MOT. A comprehensive experiment are conducted on VT-MOT and the results prove the superiority and effectiveness of the proposed method compared with state-of-the-art methods. From the evaluation results and analysis, we specify several potential future directions for visible-thermal MOT. The project is released in https://github.com/wqw123wqw/PFTrack.

Related papers

• MV-MOS: Multi-View Feature Fusion for 3D Moving Object Segmentation [4.386035726986601]
  How to effectively utilize motion and semantic features and avoid information loss during 3D-to-2D projection is still a key challenge. We propose a novel multi-view MOS model (MV-MOS) by fusing motion-semantic features from different 2D representations of point clouds. We validated the effectiveness of the proposed multi-branch fusion MOS framework via comprehensive experiments.
  arXiv  Detail & Related papers  (2024-08-20T07:30:00Z)
• Lifting Multi-View Detection and Tracking to the Bird's Eye View [5.679775668038154]
  Recent advancements in multi-view detection and 3D object recognition have significantly improved performance. We compare modern lifting methods, both parameter-free and parameterized, to multi-view aggregation. We present an architecture that aggregates the features of multiple times steps to learn robust detection.
  arXiv  Detail & Related papers  (2024-03-19T09:33:07Z)
• Single-Shot and Multi-Shot Feature Learning for Multi-Object Tracking [55.13878429987136]
  We propose a simple yet effective two-stage feature learning paradigm to jointly learn single-shot and multi-shot features for different targets. Our method has achieved significant improvements on MOT17 and MOT20 datasets while reaching state-of-the-art performance on DanceTrack dataset.
  arXiv  Detail & Related papers  (2023-11-17T08:17:49Z)
• Unifying Tracking and Image-Video Object Detection [54.91658924277527]
  TrIVD (Tracking and Image-Video Detection) is the first framework that unifies image OD, video OD, and MOT within one end-to-end model. To handle the discrepancies and semantic overlaps of category labels, TrIVD formulates detection/tracking as grounding and reasons about object categories.
  arXiv  Detail & Related papers  (2022-11-20T20:30:28Z)
• Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline [80.13652104204691]
  In this paper, we construct a large-scale benchmark with high diversity for visible-thermal UAV tracking (VTUAV) We provide a coarse-to-fine attribute annotation, where frame-level attributes are provided to exploit the potential of challenge-specific trackers. In addition, we design a new RGB-T baseline, named Hierarchical Multi-modal Fusion Tracker (HMFT), which fuses RGB-T data in various levels.
  arXiv  Detail & Related papers  (2022-04-08T15:22:33Z)
• Know Your Surroundings: Panoramic Multi-Object Tracking by Multimodality Collaboration [56.01625477187448]
  We propose a MultiModality PAnoramic multi-object Tracking framework (MMPAT) It takes both 2D panorama images and 3D point clouds as input and then infers target trajectories using the multimodality data. We evaluate the proposed method on the JRDB dataset, where the MMPAT achieves the top performance in both the detection and tracking tasks.
  arXiv  Detail & Related papers  (2021-05-31T03:16:38Z)
• Probabilistic 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving [22.693895321632507]
  We propose a probabilistic, multi-modal, multi-object tracking system consisting of different trainable modules. We show that our method outperforms current state-of-the-art on the NuScenes Tracking dataset.
  arXiv  Detail & Related papers  (2020-12-26T15:00:54Z)
• Multi-modal Visual Tracking: Review and Experimental Comparison [85.20414397784937]
  We summarize the multi-modal tracking algorithms, especially visible-depth (RGB-D) tracking and visible-thermal (RGB-T) tracking. We conduct experiments to analyze the effectiveness of trackers on five datasets.
  arXiv  Detail & Related papers  (2020-12-08T02:39:38Z)
• Dense Scene Multiple Object Tracking with Box-Plane Matching [73.54369833671772]
  Multiple Object Tracking (MOT) is an important task in computer vision. We propose the Box-Plane Matching (BPM) method to improve the MOT performacne in dense scenes. With the effectiveness of the three modules, our team achieves the 1st place on the Track-1 leaderboard in the ACM MM Grand Challenge HiEve 2020.
  arXiv  Detail & Related papers  (2020-07-30T16:39:22Z)

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.