.png){kind=icon}
返回
Robust Multi-Object Tracking With Local Appearance and Stable Motion Models
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3296731.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multi-object tracking (MOT) has been steadily studied for video understanding in computer vision. However, existing MOT frameworks usually employ straightforward appearance or motion models and may struggle in dynamic environments with similar appearance and complex motion. In this paper, we present a robust MOT framework with local appearance and stable motion models to overcome these two hindrances. The framework incorporates object and local part detectors, a feature extractor, a keypoint extractor, and a data association method. For the data association, we utilize five types of similarity metrics and a cascaded matching strategy. The local appearance model is suggested to be used additionally with global appearance features of full bounding boxes to obtain discriminative features even for objects with a similar appearance. At the same time, the stable motion model considers the core of the body as the central point of the object and subdivides the body using a novel 12-tuple Kalman state vector to analyze complex motion. As a result, our new tracker achieves state-of-the-art performance on the DanceTrack test set, surpassing all other listed tracking systems in terms of both detection and tracking quality metrics, obtaining 61.3 HOTA, 82.3 DetA, 45.8 AssA, and 91.7 MOTA. The source code is available at https://github.com/Jubi-Hwang/Robust-MOT-with-Local-Appearance-and-Stable-Motion-Models.
Keyword:
Multi-object tracking
tracking-by-detection
similarity metrics
matching strategy
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Thermal comfort, perceived air quality, and cognitive performance when personally controlled air movement is used by tropically acclimatized persons当热带适应的人使用个人控制的空气运动时，热舒适性，感知的空气质量和认知表现.png)
Indoor Air
OAAI
Indoor AirIF0
OAAI分享收藏Analysis and assessment of ship collision accidents using Fault Tree and Multiple Correspondence Analysis基于故障树和多重对应分析的船舶碰撞事故分析与评估
PREAI
分享收藏A quantitative analysis of the flora and plant communities of a representative midwestern U.S. town
PREAI
分享收藏
查看更多内容