An Analysis of Video-based Human Activity Detection Approaches

Human action detection and recognition has become a significant topic in computer vision research over the last two decades. Intelligent techniques (such as machine learning and deep learning) have grown in popularity as a result of technological advancements in visual data. Due to the exponential growth of video data generated by surveillance cameras, intelligent systems are in great demand for detecting specific human activities. In this work, we critically examine the state-of-the-art (SOA) methodologies for human activity detection (HAD) approaches. The study illustrates a generic classification for HAD methods anda paradigm shift is observed from traditional to modern deep learning-based techniques. We also provide an analysis of publicly available datasets for the classification of human activities. The paper outlines various open research issues as well as future directions for HAD methods. Due to problems such as a dynamic and complex background, camera motion, occlusion, and bad weather, it is evident that detecting human behaviors in surveillance videos is a challenging task. Convolution neural networks (CNNs) are used in the bulk of HAD techniques, prompting researchers to look at sequence learning models like Recurrent Neural Networks (RNNs) and Long-Short-Term Neural Networks (LSTM). Furthermore, our analysis indicates that only a few research articles on the detection of anomalous behavior have been published, with the majority of the work focusing on human action detection. Furthermore, existing HAD deep learning models could be improved by including the notion of transfer learning, which saves training time and enhances accuracy.