Advanced vehicle tracking systems are crucial for the development of intelligent transportation infrastructure, but existing approaches face challenges with real-time visualization, intuitive data interpretation, and effective risk assessment. This paper presents XR-VITS, an Extended Reality Vehicle Intelligent Tracking System that integrates established computer vision-based object detection (YOLO-based detector, internal variant optimized for traffic surveillance), Kalman filtering, homography mapping, and extended reality (XR) visualization techniques into a unified framework for comprehensive traffic monitoring and analysis. The primary contribution of this work lies in the systematic engineering integration of well-established algorithmic components and the comprehensive empirical validation of their combined effectiveness for operator-assisted traffic monitoring, rather than proposing novel detection or tracking algorithms. The proposed system detects and tracks multiple vehicles, maps their trajectories to real-world coordinates, predicts future paths, and assesses collision risks—all visualized through an immersive XR interface. Experimental results demonstrate that XR-VITS achieves 89.3% tracking accuracy (MOTA) while maintaining real-time performance (25 FPS) across diverse traffic conditions, including adverse weather and low-light scenarios. This work targets urban traffic monitoring scenarios where operators must rapidly interpret complex multi-vehicle interactions for safety-critical decision-making. The system’s risk assessment module shows 87.3% precision in predicting potential vehicle conflicts, with XR visualization reducing operator response time by 41.5% compared to traditional interfaces, as validated through a user study with 24 traffic management professionals. This integrated approach bridges the gap between complex traffic data and human comprehension, demonstrating practical applicability for traffic management, autonomous vehicle training, and smart city deployments.
Manzoor et al. (Wed,) studied this question.