Object Detection and Tracking Face Detection and Recognition
Varsha Kiran Patil, Pawan Nawade, Rudra Nagarkar, Paresh Kadale · 2024
Object detection and tracking, along with face recognition and detection, pose significant challenges in image processing and computer vision due to variations in size, shape, color, and appearance. Various techniques and methodologies have been developed to address these challenges, including deep learning-based methodologies, feature-based methods, and hybrid approaches. These methods rely on advanced algorithms but have ethical and privacy issues that need to be considered for creating proper standards and laws. Evaluation measures, such as accuracy, recall, F1 score, and mean average precision (MAP), are used to measure the effectiveness of object detection and tracking or face recognition models. The results of studies on these models highlight the accuracy and effectiveness of the developed and tested facial recognition and object-tracking models. However, limitations and issues with the proposed methods need to be considered, and suggestions for additional study should be made. The potential applications for tracking and detecting objects or faces in various industries should also be explored. Therefore, it is crucial to emphasize the primary goals or research issues, methodology or strategy, key conclusions, implications or possible benefits, privacy or ethical issues, and suitable rules and regulations for these technologies in the abstract.