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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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