SocialTrack: Multi-Object Tracking in Complex Urban Traffic Scenes Inspired by Social Behavior

2025-08-18

AI Summary

Multi-object tracking (MOT) in urban traffic scenes from drone viewpoints faces significant challenges, including severe scale variation of small objects, heavy occlusion, nonlinear crossing motions, and motion blur. To address these issues, this paper proposes SocialTrack—a socially inspired tracking framework. Methodologically, SocialTrack integrates multi-scale feature-enhanced small-object detection, velocity-adaptive cubature Kalman filtering (VACKF), a group motion compensation strategy (GMCS), and a spatiotemporal memory prediction mechanism (STMP), jointly modeling both individual dynamics and social group interactions to substantially mitigate identity switches. Evaluated on UAVDT and MOT17 benchmarks, SocialTrack achieves state-of-the-art performance with substantial improvements in core metrics—e.g., MOTA and IDF1—outperforming existing methods by clear margins. The framework is highly modular and compatible, enabling plug-and-play integration to enhance the performance of mainstream trackers without architectural modifications.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Security and Privacy: Tracking, profiling, and countermeasures against them
• Web Mining and Content Analysis: Topic discovery and tracking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

As a key research direction in the field of multi-object tracking (MOT), UAV-based multi-object tracking has significant application value in the analysis and understanding of urban intelligent transportation systems. However, in complex UAV perspectives, challenges such as small target scale variations, occlusions, nonlinear crossing motions, and motion blur severely hinder the stability of multi-object tracking. To address these challenges, this paper proposes a novel multi-object tracking framework, SocialTrack, aimed at enhancing the tracking accuracy and robustness of small targets in complex urban traffic environments. The specialized small-target detector enhances the detection performance by employing a multi-scale feature enhancement mechanism. The Velocity Adaptive Cubature Kalman Filter (VACKF) improves the accuracy of trajectory prediction by incorporating a velocity dynamic modeling mechanism. The Group Motion Compensation Strategy (GMCS) models social group motion priors to provide stable state update references for low-quality tracks, significantly improving the target association accuracy in complex dynamic environments. Furthermore, the Spatio-Temporal Memory Prediction (STMP) leverages historical trajectory information to predict the future state of low-quality tracks, effectively mitigating identity switching issues. Extensive experiments on the UAVDT and MOT17 datasets demonstrate that SocialTrack outperforms existing state-of-the-art (SOTA) methods across several key metrics. Significant improvements in MOTA and IDF1, among other core performance indicators, highlight its superior robustness and adaptability. Additionally, SocialTrack is highly modular and compatible, allowing for seamless integration with existing trackers to further enhance performance.

Problem

• Enhancing tracking accuracy for small targets in urban traffic
• Improving trajectory prediction with velocity dynamic modeling
• Reducing identity switches using historical trajectory data

Innovation

• Multi-scale feature enhancement for small-target detection
• Velocity Adaptive Cubature Kalman Filter for trajectory prediction
• Group Motion Compensation Strategy for target association