Comparative Performance Analysis of Deep Neural Networks for Human Activity Recognition
Muhammad Ashar Burney, Ayesha Waheed, Shuaib Khan, Sidra Ghori, Muhammad Bilal Shaikh, Rizwan Qureshi · 2023
Human activity recognition is a challenging computer vision problem, which involves identifying human activities using artificial intelligence methods, from the data collected from various sensors. In this paper, existing deep neural networks are studied and a custom deep architecture is proposed for human pose and activity recognition. The proposed architecture is mainly based on the approach of localizing human body joints in a 3D space on a single 2D image. The fundamental problem is the wide variations in appearance, caused by various camera angles which include, clothing, body shapes, and self-occlusion, and complications in background variations. The Human 3.6M and patient MoCap datasets are used as a benchmark and our proposed architecture shows competitive performance compared to the state-of-the-art methods.