The domain of Multi-Object Tracking (MOT) is of paramount significance within
the realm of video analysis. However, both traditional methodologies and deep
learning-based approaches within this domain exhibit inherent limitations. Deep
learning methods driven exclusively by data exhibit challenges in accurately
discerning the motion states of objects, while traditional methods relying on
comprehensive mathematical models may suffer from suboptimal tracking
precision. To address these challenges, we introduce the Model-Data-Driven
Motion-Static Object Tracking Method (MoD2T). We propose a novel architecture
that adeptly amalgamates traditional mathematical modeling with deep
learning-based MOT frameworks, thereby effectively mitigating the limitations
associated with sole reliance on established methodologies or advanced deep
learning techniques. MoD2T's fusion of mathematical modeling and deep learning
augments the precision of object motion determination, consequently enhancing
tracking accuracy. Our empirical experiments robustly substantiate MoD2T's
efficacy across a diverse array of scenarios, including UAV aerial surveillance
and street-level tracking. To assess MoD2T's proficiency in discerning object
motion states, we introduce MVF1 metric. This novel performance metric is
designed to measure the accuracy of motion state classification, providing a
comprehensive evaluation of MoD2T's performance. Meticulous experiments
substantiate the rationale behind MVF1's formulation. To provide a
comprehensive assessment of MoD2T's performance, we meticulously annotate
diverse datasets and subject MoD2T to rigorous testing. The achieved MVF1
scores, which measure the accuracy of motion state classification, are
particularly noteworthy in scenarios marked by minimal or mild camera motion,
with values of 0.774 on the KITTI dataset, 0.521 on MOT17, and 0.827 on UAVDT.

MoD2T 是一种结合了传统数学建模和深度学习的多对象跟踪方法，提高了物体运动确 定性和跟踪精确度；通过 MVFI 度量标准评估效果，在多种场景下都取得了显著的成绩。