Hierarchical IoU Tracking based on Interval

• URL: http://arxiv.org/abs/2406.13271v1
• Date: Wed, 19 Jun 2024 07:03:18 GMT
• Title: Hierarchical IoU Tracking based on Interval
• Authors: Yunhao Du, Zhicheng Zhao, Fei Su,
• Abstract summary: Multi-Object Tracking (MOT) aims to detect and associate all targets of given classes across frames. We propose the Hierarchical IoU Tracking framework, dubbed HIT, which achieves unified hierarchical tracking by utilizing tracklet intervals as priors. Our method achieves promising performance on four datasets, i.e., MOT17, KITTI, DanceTrack and VisDrone.
• Score: 21.555469501789577
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multi-Object Tracking (MOT) aims to detect and associate all targets of given classes across frames. Current dominant solutions, e.g. ByteTrack and StrongSORT++, follow the hybrid pipeline, which first accomplish most of the associations in an online manner, and then refine the results using offline tricks such as interpolation and global link. While this paradigm offers flexibility in application, the disjoint design between the two stages results in suboptimal performance. In this paper, we propose the Hierarchical IoU Tracking framework, dubbed HIT, which achieves unified hierarchical tracking by utilizing tracklet intervals as priors. To ensure the conciseness, only IoU is utilized for association, while discarding the heavy appearance models, tricky auxiliary cues, and learning-based association modules. We further identify three inconsistency issues regarding target size, camera movement and hierarchical cues, and design corresponding solutions to guarantee the reliability of associations. Though its simplicity, our method achieves promising performance on four datasets, i.e., MOT17, KITTI, DanceTrack and VisDrone, providing a strong baseline for future tracking method design. Moreover, we experiment on seven trackers and prove that HIT can be seamlessly integrated with other solutions, whether they are motion-based, appearance-based or learning-based. Our codes will be released at https://github.com/dyhBUPT/HIT.

Related papers

• Multi-object Tracking by Detection and Query: an efficient end-to-end manner [23.926668750263488]
  Multi-object tracking is advancing through two dominant paradigms: traditional tracking by detection and newly emerging tracking by query. We propose the tracking-by-detection-and-query paradigm, which is achieved by a Learnable Associator. Compared to tracking-by-query models, LAID achieves competitive tracking accuracy with notably higher training efficiency.
  arXiv  Detail & Related papers  (2024-11-09T14:38:08Z)
• 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)
• Towards Unified Token Learning for Vision-Language Tracking [65.96561538356315]
  We present a vision-language (VL) tracking pipeline, termed textbfMMTrack, which casts VL tracking as a token generation task. Our proposed framework serializes language description and bounding box into a sequence of discrete tokens. In this new design paradigm, all token queries are required to perceive the desired target and directly predict spatial coordinates of the target.
  arXiv  Detail & Related papers  (2023-08-27T13:17:34Z)
• Hybrid-SORT: Weak Cues Matter for Online Multi-Object Tracking [51.16677396148247]
  Multi-Object Tracking (MOT) aims to detect and associate all desired objects across frames. In this paper, we demonstrate this long-standing challenge in MOT can be efficiently and effectively resolved by incorporating weak cues. Our method Hybrid-SORT achieves superior performance on diverse benchmarks, including MOT17, MOT20, and especially DanceTrack.
  arXiv  Detail & Related papers  (2023-08-01T18:53:24Z)
• Sparse Message Passing Network with Feature Integration for Online Multiple Object Tracking [6.510588721127479]
  Experimental results demonstrate that a simple online MPN with these two contributions can perform better than many state-of-the-art methods. Our association method generalizes well and can also improve the results of private detection based methods.
  arXiv  Detail & Related papers  (2022-12-06T14:10:57Z)
• Unified Transformer Tracker for Object Tracking [58.65901124158068]
  We present the Unified Transformer Tracker (UTT) to address tracking problems in different scenarios with one paradigm. A track transformer is developed in our UTT to track the target in both Single Object Tracking (SOT) and Multiple Object Tracking (MOT)
  arXiv  Detail & Related papers  (2022-03-29T01:38:49Z)
• Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework [76.70603443624012]
  We propose a novel one-stream tracking (OSTrack) framework that unifies feature learning and relation modeling. In this way, discriminative target-oriented features can be dynamically extracted by mutual guidance. OSTrack achieves state-of-the-art performance on multiple benchmarks, in particular, it shows impressive results on the one-shot tracking benchmark GOT-10k.
  arXiv  Detail & Related papers  (2022-03-22T18:37:11Z)
• Online Multiple Object Tracking with Cross-Task Synergy [120.70085565030628]
  We propose a novel unified model with synergy between position prediction and embedding association. The two tasks are linked by temporal-aware target attention and distractor attention, as well as identity-aware memory aggregation model.
  arXiv  Detail & Related papers  (2021-04-01T10:19:40Z)

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.