A Comprehensive Review of Real-Time Shape Detection and Object Tracking Techniques in Computer Vision

Real time shape determination and tracking objects are the main components of modern computer vision that lead to the range of high-level uses that include autonomous navigation, robotics, smart surveillance, and human-computer interfaces. Given the growing dependence on autonomous systems, there is an increasingly pressing need to have algorithms that can provide supreme accuracy and computational latency at the same time. The review provides an in-depth discussion of the development of shapes detection and object tracking methodologies and its latest state-of-the-art methods, focusing on real-time operation. It is critical of usage transforming traditional methods of image processing, like edge detection and Kalman filter, to deep learning models, like Convolutional Neural Networks (CNNs) and more recent models like Vision Transformers (ViTs). Moreover, the review groups and analyzes popular open-source datasets, such as COCO, MOTChallenge, and KITTI, and outlines the consequences of owned data sources. Quantitative empirical evaluation of the integrated systems (Tracking-byDetection and Joint Detection and Embedding (JDE)) is given and supported with the quantitative performance measures like Mean Average Precision (mAP) and Multi-Object Tracking Accuracy (MOTA). Lastly, the paper establishes the enduring issues of occlusion, variation of scales, deployment of edges, and suggests future research avenues that could help solve the gap between theory and practical applications.