  In the realm of video analysis, the field of multiple object tracking (MOT)
assumes paramount importance, with the motion state of objects-whether static
or dynamic relative to the ground-holding practical significance across diverse
scenarios. However, 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 suboptimal tracking
accuracy. To address these challenges, we introduce a Model-Data-Driven Motion
State Judgment Object Tracking Method (MoD2T). This innovative architecture
adeptly amalgamates traditional mathematical modeling with deep learning-based
multi-object tracking 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. This
novel performance metric aims to quantitatively assess the accuracy of motion
state classification, furnishing a comprehensive evaluation of MoD2T's
performance. Elaborate experimental validations corroborate the rationality of
MVF1. In order to holistically appraise MoD2T's performance, we meticulously
annotate several renowned datasets and subject MoD2T to stringent testing.
Remarkably, under conditions characterized by minimal or moderate camera
motion, the achieved MVF1 values are particularly noteworthy, with exemplars
including 0.774 for the KITTI dataset, 0.521 for MOT17, and 0.827 for UAVDT.
