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.
Highlights
INTRODUCTIONWith the increasing number of anti-social activities that have been taking place, security has been given utmost importance off late and it is paramount that every citizen plays his share in warranting the safeguarding of our society
With the increasing number of anti-social activities that have been taking place, security has been given utmost importance off late and it is paramount that every citizen plays his share in warranting the safeguarding of our society.Many organizations have mounted CCTV cameras for the constant monitoring and invigilation of people in public areas and their interactions
Since constant monitoring of data by humans to judge if the events are abnormal is a near impossible task and requires a colossal workforce and constant attention and awareness, it calls for a need to automate the same
Summary
With the increasing number of anti-social activities that have been taking place, security has been given utmost importance off late and it is paramount that every citizen plays his share in warranting the safeguarding of our society. Contextual Anomaly: The abnormality in this case is context definitive. This type of anomaly is frequent in time-series data. Manual detection of anomalous and abnormal events in long series of video data, such as surveillance tapes, requires a great deal of manpower that might not be available at all times to all organisations. There are copious successful cases where anomaly detection has worked well[1,2,7]. These methods work by exploiting labelled data which is infeasible and costly. This demands for an approach that is increasingly feasible to implement and doesn’t burden the programmer
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