Collaborative Tracking Learning for Frame-Rate-Insensitive Multi-Object Tracking

• URL: http://arxiv.org/abs/2308.05911v2
• Date: Tue, 12 Sep 2023 14:01:07 GMT
• Title: Collaborative Tracking Learning for Frame-Rate-Insensitive Multi-Object Tracking
• Authors: Yiheng Liu, Junta Wu, Yi Fu
• Abstract summary: Multi-object tracking (MOT) at low frame rates can reduce computational, storage and power overhead to better meet the constraints of edge devices. We propose to explore collaborative tracking learning (ColTrack) for frame-rate-insensitive MOT in a query-based end-to-end manner.
• Score: 3.781471919731034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-object tracking (MOT) at low frame rates can reduce computational, storage and power overhead to better meet the constraints of edge devices. Many existing MOT methods suffer from significant performance degradation in low-frame-rate videos due to significant location and appearance changes between adjacent frames. To this end, we propose to explore collaborative tracking learning (ColTrack) for frame-rate-insensitive MOT in a query-based end-to-end manner. Multiple historical queries of the same target jointly track it with richer temporal descriptions. Meanwhile, we insert an information refinement module between every two temporal blocking decoders to better fuse temporal clues and refine features. Moreover, a tracking object consistency loss is proposed to guide the interaction between historical queries. Extensive experimental results demonstrate that in high-frame-rate videos, ColTrack obtains higher performance than state-of-the-art methods on large-scale datasets Dancetrack and BDD100K, and outperforms the existing end-to-end methods on MOT17. More importantly, ColTrack has a significant advantage over state-of-the-art methods in low-frame-rate videos, which allows it to obtain faster processing speeds by reducing frame-rate requirements while maintaining higher performance. Code will be released at https://github.com/yolomax/ColTrack

Related papers

• Temporal Correlation Meets Embedding: Towards a 2nd Generation of JDE-based Real-Time Multi-Object Tracking [52.04679257903805]
  Joint Detection and Embedding (JDE) trackers have demonstrated excellent performance in Multi-Object Tracking (MOT) tasks. Our tracker, named TCBTrack, achieves state-of-the-art performance on multiple public benchmarks.
  arXiv  Detail & Related papers  (2024-07-19T07:48:45Z)
• Exploring Dynamic Transformer for Efficient Object Tracking [58.120191254379854]
  We propose DyTrack, a dynamic transformer framework for efficient tracking. DyTrack automatically learns to configure proper reasoning routes for various inputs, gaining better utilization of the available computational budget. Experiments on multiple benchmarks demonstrate that DyTrack achieves promising speed-precision trade-offs with only a single model.
  arXiv  Detail & Related papers  (2024-03-26T12:31:58Z)
• SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depth [84.64121608109087]
  We propose a pseudo-depth estimation method for obtaining the relative depth of targets from 2D images. Secondly, we design a depth cascading matching (DCM) algorithm, which can use the obtained depth information to convert a dense target set into multiple sparse target subsets. By integrating the pseudo-depth method and the DCM strategy into the data association process, we propose a new tracker, called SparseTrack.
  arXiv  Detail & Related papers  (2023-06-08T14:36:10Z)
• Tracking by Associating Clips [110.08925274049409]
  In this paper, we investigate an alternative by treating object association as clip-wise matching. Our new perspective views a single long video sequence as multiple short clips, and then the tracking is performed both within and between the clips. The benefits of this new approach are two folds. First, our method is robust to tracking error accumulation or propagation, as the video chunking allows bypassing the interrupted frames. Second, the multiple frame information is aggregated during the clip-wise matching, resulting in a more accurate long-range track association than the current frame-wise matching.
  arXiv  Detail & Related papers  (2022-12-20T10:33:17Z)
• Towards Frame Rate Agnostic Multi-Object Tracking [76.82407173177138]
  We propose a Frame Rate Agnostic MOT framework with a Periodic training Scheme (FAPS) to tackle the FraMOT problem for the first time. Specifically, we propose a Frame Rate Agnostic Association Module (FAAM) that infers and encodes the frame rate information. FAPS reflects all post-processing steps in training via tracking pattern matching and fusion.
  arXiv  Detail & Related papers  (2022-09-23T04:25:19Z)
• Cannot See the Forest for the Trees: Aggregating Multiple Viewpoints to Better Classify Objects in Videos [36.28269135795851]
  We present a set classifier that improves accuracy of classifying tracklets by aggregating information from multiple viewpoints contained in a tracklet. By simply attaching our method to QDTrack on top of ResNet-101, we achieve the new state-of-the-art, 19.9% and 15.7% TrackAP_50 on TAO validation and test sets.
  arXiv  Detail & Related papers  (2022-06-05T07:51:58Z)
• Real-time Online Multi-Object Tracking in Compressed Domain [66.40326768209]
  Recent online Multi-Object Tracking (MOT) methods have achieved desirable tracking performance. Inspired by the fact that the adjacent frames are highly relevant and redundant, we divide the frames into key and non-key frames. Our tracker is about 6x faster while maintaining a comparable tracking performance.
  arXiv  Detail & Related papers  (2022-04-05T09:47:24Z)
• VariabilityTrack:Multi-Object Tracking with Variable Speed Object Movement [1.6385815610837167]
  Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. We propose a variable speed Kalman filter algorithm based on environmental feedback and improve the matching process.
  arXiv  Detail & Related papers  (2022-03-12T12:39:41Z)

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.