Dr. Jinxuan Zhu Profile

Dr. Jinxuan Zhu

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Proceedings Article | 7 March 2024 Paper

A multi-object tracking method combining high-order modeling, future position feature revision, and conceptual features

Kang Luo, Xinfang Zhang, Guoyou Wang, Jinxuan Zhu

Proceedings Volume 13085, 130850L (2024) https://doi.org/10.1117/12.3005358

KEYWORDS: Modeling, Detection and tracking algorithms, Mathematical optimization, Computer vision technology

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Multiple Object Tracking (MOT) which is an important research topic in computer vision, plays an important role in the fields of automatic driving and area monitoring. In the object dense scene, there is a phenomenon of occlusion between a large number of object. A large number of locally visible object lead to the degradation of the tracking performance of the multi-object tracking algorithm in this scene. In this paper, we propose a method that combines high-order modeling, future location feature revision, and conceptual features to address the missing object re-matching problem. Higher-order modeling enables more accurate approximations of actual functions. It modifies the current prediction with one or more features of future locations. The overall feature of one object is composed of multiple local conceptual features. The object can be expressed by the combination of several concept features when it is greater than a certain similarity threshold. Experimental results show that the above three optimization mechanisms can effectively alleviate the problems of multiobject tracking algorithms in dense object scenes, and the optimized algorithm has significantly improved accuracy in multiple tracking scenarios.

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