ODC-net: Scalable and efficient object detection and classification in multi-object CCTV environments using deep transformer YOLO

The increasing demand for real-time, accurate, and scalable surveillance systems is driven by the rapid rise in urban Closed-Circuit Television (CCTV) deployments, with global video surveillance expected to generate over 3 billion video hours daily. However, existing multi-object detection and classification approaches struggle with poor scalability, temporal inconsistencies, and sub-optimal accuracy, particularly in complex CCTV environments with dynamic object motion. To address these limitations, this work proposes Object Detection Classification Network (ODC Net), a scalable and efficient framework for multi-object detection, classification, and tracking in CCTV video streams. The model leverages the MS-COCO-2017 dataset for robust training on diverse object categories, followed by a novel Hierarchical Spatial Temporal Aggregation (HSTA) feature extraction technique that enhances spatio-temporal consistency and contextual learning. The core detection and classification module are powered by Deep Transformer You Only Look at Once V8 (DT- YOLOV8), combining transformer attention mechanisms with YOLO's real-time detection capabilities. During testing, input CCTV videos undergo preprocessing through video-to-frame conversion, with each frame processed using HSTA and DT- YOLOV8 for precise object detection and classification. Furthermore, to ensure reliable object tracking across video frames, a Feature Adaptive Continual-Learning Tracker (FACLT) is integrated, enabling consistent object association and high-quality output video generation with real-time annotations. Extensive experiments demonstrate the superior performance of ODC Net, achieving an impressive 99.477 % accuracy, 99.334 % precision, 99.409 % recall, and 99.727 % F1-score, establishing its effectiveness for real-world multi-object surveillance in dynamic CCTV environments.