Artificial Intelligence for Low Level Suspicious Activity Detection

Abstract

Human activity detection using video surveillance system is the processing of consecutive video frames and analysing any suspicious activity occurring in the video. Detection of human activities comes under the areas of artificial intelligence with image analysis and computer vision as sub-domain. The paper deals with detection of harmful or troublesome human activities using the matching of features obtained by SIFT, so that the further suspicious action can be prevented by alerting the concerned system. The paper narrates about the detection of suspicious activity such as holding a gun, wielding a knife, or punching based on their features. The proposed approach analyses the video frame by frame by observing the suspicious object as well as its activities in the video, and implements SIFT for extraction of features and mean shift algorithm (MSA) for object tracking. This paper gives an apparent idea of developing the system detecting suspicious activities of humans.