Anomaly Detection in Human Behavior using Video Surveillance
Neha Sharma1, Pradeep Kumar D2, Rohit Kumar3, Shiv Dutt Tripathi4
1Neha Sharma, Computer Science and Engineering, Ramaiah Institute of Technology, Bangalore, India.
2Mr. Pradeep Kumar D., Computer Science and Engineering, Ramaiah Institute of Technology, Bangalore, India.
3Shiv Dutt Tripathi, Computer Science and Engineering, Ramaiah Institute of Technology, Bangalore, India.
4Rohit Kumar, Computer Science and Engineering, Ramaiah Institute of Technology, Bangalore, India.
Manuscript received on November 25, 2019. | Revised Manuscript received on December 08, 2019. | Manuscript published on December 30, 2019. | PP: 328-332 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3133129219/2019©BEIESP | DOI: 10.35940/ijeat.B3133.129219
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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: Conventional static surveillance has proved to be quite ineffective as the huge number of cameras to keep an eye on most often outstrips the monitor’s ability to do so. Furthermore, the amount of focus needed to constantly monitor the surveillance video cameras is often overbearing. The review paper focuses on solving the problem of anomaly detection in video sequence through semi-supervised techniques. Each video is defined as sequence of frames. The model is trained with goal to minimize the reconstruction error which later on is used to detect anomaly in the test sample videos. The model was trained and tested on most commonly used benchmarking datasetAvenue dataset. Experiment results confirm that the model detects anomaly in a video with a reasonably good accuracy in presence of some noise in dataset.
Keywords: Video surveillance, anomaly detection, semi-supervised learning, unusual activity, video processing, abnormal behavior.