Object Detection and Tracking using Deep Learning and Artificial Intelligence for Video Surveillance Applications
Keywords:
Object Detection , CNN , python , surveillance, real time trackingAbstract
In the evolving landscape of intelligent surveillance systems, object detection and tracking play a critical role in enhancing situational awareness and automated decision-making. This research presents an efficient deep learning-based framework for real-time object detection and tracking within video surveillance environments, leveraging the strengths of Convolutional Neural Networks (CNN) and You Only Look Once version 3 (YOLOv3). The system is trained on urban vehicle datasets and evaluated using KITTI and COCO datasets, enabling both single and multiple object detection. Key performance metrics such as accuracy, precision, confusion matrix, and mean Average Precision (mAP) are utilized for validation. Detected objects are further tracked across consecutive frames using Simple Online Real-time Tracking (SORT), enabling trajectory analysis. The framework's robustness in varying lighting and environmental conditions makes it suitable for applications in traffic density estimation, autonomous vehicle navigation, and smart city infrastructure. Comparative analysis with existing models underscores the proposed method's capability in achieving accurate, fast, and real-time tracking, highlighting its potential for deployment in modern intelligent transport systems.
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