TY  - JOUR
PY  - 2019//
TI  - Anomaly detection in human behavior using video surveillance
JO  - International journal of engineering and advanced technology
A1  - Sharma, Neha
A1  - Kumar, Pradeep
A1  - Kumar, Rohit
A1  - Tripathi, Shiv Dutt
SP  - 328
EP  - 332
VL  - 9
IS  - 2
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2249-8958
UR  - http://dx.doi.org/10.35940/ijeat.B3133.129219
ID  - ref1
ER  -