Deep Learning Techniques for Human Abnormal Activity Recognition using Video Feature Extraction and Analysis: A Systematic Review
Vithya Ganesan Sunitha.S · Solid State Technology · 2020
In recent times, deep learning techniques has shown its ability to apply in any field includingspeech recognition, image/video processing, natural language processing, and many more real-life problemssolving. On the other side Human activity recognition (HAR) has become a popular topic in research due toits broad application for scientists and engineers. The researchers started working on the new ideas byintegrating the two emerging areas to solve HAR problems with the implementation of deep learning. Thepresent paper has reviewed some of the literatures related to existing HAR systems that are used torecognize the human activities with prominence on detecting abnormal behaviors. First, a review isconducted to understand how the deep features can be extracted and examined acquired from the videoframes. Second, a review is conducted to understand the process of analysis to understand the humanactivities. Third, a review is conducted to understand how the existing HAR models or systems arecontributing to the efficient recognition of human activities in real-time scenarios. Forth, the complexitieswhile implementing the HAR systems are represented as challenges and mentioned finally the future scopefor analysis of input videos for abnormalities detection in human behaviour. The review of literature is topermit the researchers to learn new techniques of deep learning while identifying the activities by enhancingthe systems performance accuracy.