# Dynamic Collaborative Optimization Method for Real-Time Multi-Object Tracking
## 1 Introduction  
Multi-Object Tracking (MOT), a critical computer vision task, finds extensive applications in intelligent transportation, security surveillance, and human-machine interactions [1]. As deep learning advances, the Tracking-by-Detection paradigm has emerged as the predominant approach [2], yet it faces significant challenges in practical scenarios, as follows: (1) small object detection and representation difficulties, where conventional feature extraction techniques struggle to retain discriminative information [3]; (2) frequent identity switches in dense environments, exacerbated by mutual object occlusion leading to feature degradation [4]; and (3) imprecise non-linear motion prediction, challenging traditional motion models to effectively interpret complex motion dynamics [5]. Current tracking methods exhibit significant limitations in addressing the aforementioned challenges [1]. In terms of feature extraction, mainstream tracking models employ additional feature extraction networks, which not only demonstrate limited capability inrepresenting small objects and capturing fine-grained features but also consume considerable tracking time [6]. Regarding object association, methods like DeepSORT rely on local feature matching, failing to establish global semantic connections, which results in insufficient identity preservation in occlusion scenarios [7]. For motion prediction, SORT and its variants typically utilize linear Kalman filtering, which yields relatively low prediction accuracy for non-linear motion trajectories, particularly in scenarios with abrupt movements. Furthermore, most methods lack effective modality fusion strategies, making it difficult to balance the contributions of appearance and motion information across different scenarios [8]. Despite the mentioned advances, existing MOT methods face critical limitations in three key areas. For small object detection, conventional approaches suffer from severe feature degradation through downsampling operations, with detection accuracy dropping by up to 40% for objects smaller than 30 \(\times\) 30 pixels due to insufficient receptive field adaptation. Occlusion handling remains particularly challenging, as current association algorithms lack robust temporal consistency mechanisms, leading to identity preservation failures when occlusions persist beyond 1.5 s or affect more than 25% of target objects simultaneously. Regarding motion prediction, traditional linear models fundamentally fail to capture acceleration variations and direction changes, resulting in trajectory estimation errors increasing exponentially for objects with non-linear motion patterns, especially in crowded scenes where complex interactions frequently occur. To address these critical challenges, this paper introduces a multi-modal fusion object tracking framework, presenting the following four primary innovative contributions:  
1. Multi-Scale Feature Adaptive Enhancement Module (MS-FAE): A comprehensive global semantic association network leveraging grouped cross-attention mechanisms for efficient inter-frame feature interaction. The module incorporates an innovative memory recall mechanism to mitigate feature degradation from short-term occlusions, thereby augmenting system robustness in dense scene environments. 2. Cross-Frame Feature Association Module (CFAM): A comprehensive global semantic association network leveraging grouped cross-attention mechanisms for efficient inter-frame feature interaction. The module incorporates an innovative memory recall mechanism to mitigate feature degradation from short-term occlusions, thereby augmenting system robustness in dense scene environments. 3. Dynamic Motion Model (DMM): An advanced Kalman filter framework extension that introduces acceleration components and implements a data-driven noise adjustment strategy. Coupled with a multi-hypothesis trajectory backtracking mechanism, this approach significantly improves prediction accuracy for non-linear and abrupt motion scenarios. 4.