Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.04327 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917722071236608 |
|---|---|
| author | Nguyen-Quang, Thuc Tran, Minh-Triet |
| author_facet | Nguyen-Quang, Thuc Tran, Minh-Triet |
| contents | Multi-object tracking (MOT) in computer vision remains a significant challenge, requiring precise localization and continuous tracking of multiple objects in video sequences. The emergence of data sets that emphasize robust reidentification, such as DanceTrack, has highlighted the need for effective solutions. While memory-based approaches have shown promise, they often suffer from high computational complexity and memory usage due to storing feature at every single frame. In this paper, we propose a novel memory-based approach that selectively stores critical features based on object motion and overlapping awareness, aiming to enhance efficiency while minimizing redundancy. As a result, our method not only store longer temporal information with limited number of stored features in the memory, but also diversify states of a particular object to enhance the association performance. Our approach significantly improves over MOTRv2 in the DanceTrack test set, demonstrating a gain of 2.0% AssA score and 2.1% in IDF1 score. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04327 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TF-SASM: Training-free Spatial-aware Sparse Memory for Multi-object Tracking Nguyen-Quang, Thuc Tran, Minh-Triet Computer Vision and Pattern Recognition Multi-object tracking (MOT) in computer vision remains a significant challenge, requiring precise localization and continuous tracking of multiple objects in video sequences. The emergence of data sets that emphasize robust reidentification, such as DanceTrack, has highlighted the need for effective solutions. While memory-based approaches have shown promise, they often suffer from high computational complexity and memory usage due to storing feature at every single frame. In this paper, we propose a novel memory-based approach that selectively stores critical features based on object motion and overlapping awareness, aiming to enhance efficiency while minimizing redundancy. As a result, our method not only store longer temporal information with limited number of stored features in the memory, but also diversify states of a particular object to enhance the association performance. Our approach significantly improves over MOTRv2 in the DanceTrack test set, demonstrating a gain of 2.0% AssA score and 2.1% in IDF1 score. |
| title | TF-SASM: Training-free Spatial-aware Sparse Memory for Multi-object Tracking |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.04327 |