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Vehicle Load Monitoring (VLM) on entire long-span bridge decks presents significant challenges due to the spatial and temporal randomness of vehicles. Existing VLM systems often suffer from limited viewing coverage and poor continuity of multi-vehicle tracking methods. This paper proposes a VLM system consisting of multi-vision image pre-processing, modified YOLO-v4 model, kinematics-enhanced vehicle tracking algorithm, and data fusion method between vision and Weigh-In-Motion sub-systems. The system was tested on a long-span bridge using six cameras, achieving vehicle monitoring of entire deck. The precision of multi-vehicle tracking achieved 99.28% built upon YOLO-v4 model with 96.2% mean Average Precision (mAP). Comparative results demonstrate that the modified YOLO-v4 model outperforms state-of-the-art approaches, and our proposed tracking method surpasses other methods. Our proposed system offers a comprehensive solution for VLM on entire bridge deck, overcoming the limitations of existing methods. Future work could extend the system's capability to include complex traffic patterns. [Display omitted] • Multi-view video data are seamlessly integrated to broaden limited monitoring field. • Original YOLO-v4 is modified, achieving SOTA performance in vehicle detection. • Kinematics-based strategies are exploited to achieve high-precision vehicle tracking. • Intersection principle is proposed, fusing vision-WIM data accurately. [ABSTRACT FROM AUTHOR]