Abnormal Human Activity Detection using Unsupervised Machine Learning Techniques
Mounika Chalapati1, A. Raghuvira Pratap2

1Mounika Chalapati, CSE, V R Siddhartha Engineering College, Vijayawada, India.
2A Raghuvira Pratap, CSE, V R Siddhartha Engineering College, Vijayawada, India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 25, 2020. | Manuscript published on March 30, 2020. | PP: 3949-3953 | Volume-8 Issue-6, March 2020. | Retrieval Number: F8993038620/2020©BEIESP | DOI: 10.35940/ijrte.F8993.038620

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: 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.
Keywords: Abnormal Human Activity, Classification, Crowd, Feature Extraction, Unsupervised..
Scope of the Article: Human Computer Interactions.