Open AccessAbnormal Human Activity Detection using Unsupervised Machine Learning Techniques
Author(s) -
Mounika Chalapati,
A Raghuvira Pratap
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f8993.038620
Subject(s) - computer science , artificial intelligence , popularity , machine learning , representation (politics) , feature extraction , motion (physics) , crowd psychology , feature (linguistics) , unsupervised learning , object detection , pattern recognition (psychology) , computer vision , psychology , social psychology , linguistics , philosophy , politics , political science , law
Nowadays there is a significant study effort due to the popularity of CCTV to enhance analysis methods for surveillance videos and video-based images in conjunction with machine learning techniques for the purpose of independent assessment of such information sources. Although recognition of human intervention in computer vision is extremely attained subject, abnormal behavior detection is lately attracting more research attention. In this paper, we are interested in the studying the two main steps that compose abnormal human activity detection system which are the behavior representation and modelling. And we use different techniques, related to feature extraction and description for behavior representation as well as unsupervised classification methods for behavior modelling. In addition, available datasets and metrics for performance evaluation will be presented. Finally, this paper will be aimed to detect abnormal behaved object in crowd, such as fast motion in a crowd of walking people