Evaluation of human action recognition techniques intended for video analytics in Python

Human Action Recognition (HAR) in video plays a vital role in today's world. The aim of the HAR is to build a self-analysis system for on-going events from video data and understand the behavior of a person. This is the key functionality of intelligent video surveillance system and has wide range of applications. The applications include visual surveillance systems, robotics, health-care systems, human-computer interaction, ambient intelligence, video indexing, traffic management etc. that include interaction between humans and objects. Smart surveillance systems that can aid the human operator in real-time threat detection can be developed by applying video Analytics. Video analytics helps in interpreting the video to identify and decide spatial amp; temporal events not based on a one image. There are various methods to recognize actions and complex activities in a video which are reviewed in this paper. Actions are distinguished by simple motion pattern executed by a single human such as walking, running, hand-waving etc.