REAL TIME OBJECT AND TRACKING USING DEEP LEARNING AND OPENCV

It explores the development of Real-time object -detection and tracking have become essential components in various applications, including surveillance, autonomous vehicles, and human-computer interaction. This paper presents an efficient framework leveraging deep learning and OpenCV for real-time object detection and tracking. We employ state-of-theart convolutional neural networks (CNNs), such as YOLO (You Only Look Once) or SSD(Single Shot MultiBox Detector), to achieve highaccuracy in detecting multiple objects in dynamic environments. The system is designed to process video streams in real time,ensuring minimal latency.For tracking, we integrate algorithms like the Kalman filter or SORT (Simple Online and Realtime Tracking) to maintain the identity of detected objects across frames. The proposed method is evaluated on standard datasets, demonstrating robust performance in diverse scenarios, including varying lighting conditions and occlusions. Our results indicate that the combination of deep learning techniques with OpenCV significantly enhances the reliability and efficiency of real-time object detection and tracking systems,paving the way for more advanced applications in the future.