Hard to Track Objects with Irregular Motions and Similar Appearances?
Make It Easier by Buffering the Matching Space
- URL: http://arxiv.org/abs/2211.14317v3
- Date: Tue, 7 Nov 2023 13:05:17 GMT
- Title: Hard to Track Objects with Irregular Motions and Similar Appearances?
Make It Easier by Buffering the Matching Space
- Authors: Fan Yang, Shigeyuki Odashima, Shoichi Masui, Shan Jiang
- Abstract summary: We propose a Cascaded Buffered IoU (C-BIoU) tracker to track objects with irregular motions and indistinguishable appearances.
Despite its simplicity, our C-BIoU tracker works surprisingly well and achieves state-of-the-art results on MOT datasets.
- Score: 4.648877159175779
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a Cascaded Buffered IoU (C-BIoU) tracker to track multiple objects
that have irregular motions and indistinguishable appearances. When appearance
features are unreliable and geometric features are confused by irregular
motions, applying conventional Multiple Object Tracking (MOT) methods may
generate unsatisfactory results. To address this issue, our C-BIoU tracker adds
buffers to expand the matching space of detections and tracks, which mitigates
the effect of irregular motions in two aspects: one is to directly match
identical but non-overlapping detections and tracks in adjacent frames, and the
other is to compensate for the motion estimation bias in the matching space. In
addition, to reduce the risk of overexpansion of the matching space, cascaded
matching is employed: first matching alive tracks and detections with a small
buffer, and then matching unmatched tracks and detections with a large buffer.
Despite its simplicity, our C-BIoU tracker works surprisingly well and achieves
state-of-the-art results on MOT datasets that focus on irregular motions and
indistinguishable appearances. Moreover, the C-BIoU tracker is the dominant
component for our 2-nd place solution in the CVPR'22 SoccerNet MOT and ECCV'22
MOTComplex DanceTrack challenges. Finally, we analyze the limitation of our
C-BIoU tracker in ablation studies and discuss its application scope.
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) - Real-time Multi-Object Tracking Based on Bi-directional Matching [0.0]
This study offers a bi-directional matching algorithm for multi-object tracking.
A stranded area is used in the matching algorithm to temporarily store the objects that fail to be tracked.
In the MOT17 challenge, the proposed algorithm achieves 63.4% MOTA, 55.3% IDF1, and 20.1 FPS tracking speed.
arXiv Detail & Related papers (2023-03-15T08:38:08Z) - 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) - The Second-place Solution for ECCV 2022 Multiple People Tracking in
Group Dance Challenge [6.388173902438571]
method mainly includes two steps: online short-term tracking using our Cascaded Buffer-IoU (C-BIoU) Tracker, and, offline long-term tracking using appearance feature and hierarchical clustering.
Our C-BIoU tracker adds buffers to expand the matching space of detections and tracks.
After using our C-BIoU for online tracking, we applied the offline refinement introduced by ReMOTS.
arXiv Detail & Related papers (2022-11-24T10:04:09Z) - Probabilistic Tracklet Scoring and Inpainting for Multiple Object
Tracking [83.75789829291475]
We introduce a probabilistic autoregressive motion model to score tracklet proposals.
This is achieved by training our model to learn the underlying distribution of natural tracklets.
Our experiments demonstrate the superiority of our approach at tracking objects in challenging sequences.
arXiv Detail & Related papers (2020-12-03T23:59:27Z) - Tracklets Predicting Based Adaptive Graph Tracking [51.352829280902114]
We present an accurate and end-to-end learning framework for multi-object tracking, namely textbfTPAGT.
It re-extracts the features of the tracklets in the current frame based on motion predicting, which is the key to solve the problem of features inconsistent.
arXiv Detail & Related papers (2020-10-18T16:16:49Z) - MAT: Motion-Aware Multi-Object Tracking [9.098793914779161]
In this paper, we propose Motion-Aware Tracker (MAT), focusing more on various motion patterns of different objects.
Experiments on MOT16 and MOT17 challenging benchmarks demonstrate that our MAT approach can achieve the superior performance by a large margin.
arXiv Detail & Related papers (2020-09-10T11:51:33Z) - Appearance-free Tripartite Matching for Multiple Object Tracking [6.165592821539306]
Multiple Object Tracking (MOT) detects the trajectories of multiple objects given an input video.
Most existing algorithms depend on the uniqueness of the object's appearance, and the dominating bipartite matching scheme ignores the speed smoothness.
We propose an appearance-free tripartite matching to avoid the irregular velocity problem of the bipartite matching.
arXiv Detail & Related papers (2020-08-09T02:16:44Z) - 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) - Tracking Road Users using Constraint Programming [79.32806233778511]
We present a constraint programming (CP) approach for the data association phase found in the tracking-by-detection paradigm of the multiple object tracking (MOT) problem.
Our proposed method was tested on a motorized vehicles tracking dataset and produces results that outperform the top methods of the UA-DETRAC benchmark.
arXiv Detail & Related papers (2020-03-10T00:04:32Z)
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.