Authors : Dr. N. Leelavathy; Sudipta Akash; Md. Sohail Ansari; Rayudu Navya Sri

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/45b4pvpa

Scribd : https://tinyurl.com/bdpspywn

DOI : https://doi.org/10.38124/ijisrt/26apr358

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Multi-camera target tracking remains a fundamental challenge in intelligent video surveillance, particularly when maintaining consistent identity associations across spatially disjoint camera views with overlapping or non-overlapping fields of view. This paper introduces VisionNet, a unified deep learning framework that integrates state-of-the-art detection, single-camera tracking, and cross-camera re-identification into a cohesive and computationally efficient pipeline. VisionNet employs YOLOv8 for high-accuracy object detection, Deep Simple Online and Realtime Tracking (DeepSORT) for intracamera trajectory persistence, and Omni-Scale Network (OSNet) for discriminative cross-camera identity association. The framework leverages GPU-accelerated inference to sustain real-time throughput while preserving tracking fidelity under challenging surveillance conditions such as occlusion, lighting variation, and crowded scenes. A Flask-based monitoring dashboard provides operators with live visualization of tracked targets, inter-camera handoff events, and aggregate behavioral analytics. Extensive evaluation on benchmark datasets including MOT17, DukeMTMC-reID, and Market-1501 demonstrates that VisionNet achieves an IDF1 score of 76.4%, a MOTA of 73.9%, and a Rank-1 re-identification accuracy of 96.2%, outperforming competing baselines on identity-switch minimization. The modular architecture facilitates seamless integration with existing closed-circuit television (CCTV) infrastructure and supports diverse application domains including intelligent security systems, crowd flow analytics, retail behavior monitoring, and multi-athlete sports event tracking.

Keywords : Multi-Camera Tracking, Person Re-Identification, YOLOv8, DeepSORT, OSNet, Video Surveillance, Deep Learning.

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