Comparative analysis of Human Activity Recognition and object detection

Abstract

Human Activity Recognition (HAR) is the ability to interpret human body gesture or motion and determine human activity or action. Most of the tasks can be done automatically if they can be recognized via HAR system. HAR is an essential part of various scientific research contexts and the daily defence system i.e. surveillance, healthcare and human computer interaction (HCI). In computer vision, images and videos are analysed using different techniques. While recognition of activity in the real time environment the speed of the technique matters because if the system can detect the object in the images and videos at real time then it can be forwarded for the activity recognition process. How correctly the technique detects the object and differentiates the human object and other object and how much time it is taking to give the correct object, the number of frames it processes per second all matters. This paper deals with the comparison of the performance of YOLO (You Only Look Once) and Faster R-CNN in recognizing the human activity and detecting object.