Detecting human activities from images and videos

The detection of human activities is crucial for effective monitoring purposes. The challenge lies in accurately and promptly identifying various types of activities from videos and images captured in diverse, real-world environments. Both classical machine learning methods and deep learning techniques can be employed to tackle this task. This project aims to achieve several objectives: 1) Conducting a comprehensive literature review on existing methods for human activity detection across various applications; 2) Implementing and comparing state-of-the-art techniques for activity detection using publicly available datasets; 3) Proposing algorithms aimed at improving the accuracy of human activity detection; 4) Implementing these algorithms, evaluating their performance empirically, and comparing them with existing approaches.