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Enhanced Multi-Object Tracking: Inferring Motion States of Tracked Objects
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DOI：10.1145/3699960.png)
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Abstract
En 中文
Multi-Object Tracking (MOT) is a critical problem in computer vision, yet current research on tracking the motion state of objects relative to the ground remains limited. The extant literature exhibits a notable dearth in the exploration of this aspect. Deep learning methodologies encounter challenges in accurately discerning object motion states, while conventional approaches reliant on comprehensive mathematical modeling may yield sub-optimal tracking accuracy. To address these challenges, we introduce a Model-Data-Driven Motion State Judgment Object Tracking (MoD2T) method. This innovative architecture adeptly amalgamates classical mathematical modeling with deep learning-based MOT frameworks. The integration of mathematical modeling and deep learning within MoD2T enhances the precision of object motion state determination, thereby elevating tracking accuracy. Our empirical investigations comprehensively validate the efficacy of MoD2T across varied scenarios, encompassing unmanned aerial vehicle surveillance and street-level tracking. Furthermore, to gauge the method's adeptness in discerning object motion states, we introduce the Motion State Validation F1 (MVF1) metric.
Keywords:
Multi-object tracking
Motion state
Fusion
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