Open AccessAnomaly Detection in Human Behavior using Video Surveillance
Author(s) -
Neeraj Sharma,
Pradeep Kumar D,
Rohit Kumar,
Sandeep Kumar Tripathi
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.b3133.129219
Subject(s) - anomaly detection , computer science , benchmarking , artificial intelligence , sequence (biology) , anomaly (physics) , computer vision , focus (optics) , sample (material) , physics , marketing , biology , optics , business , genetics , condensed matter physics , chemistry , chromatography
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.