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Research Paper | Computer Engineering | India | Volume 9 Issue 2, February 2020

Suspicious Event Detection in Examination Hall

Aditya Kulkarni | Amit Dhawale | Sagar Kolhe | Amol Sawant

Abstract: Suspicious human activity recognition from surveillance video is an active research area of image processing and computer vision. Through the visual surveillance, human activities can be monitored in sensitive and public areas such as bus stations, railway stations, airports, banks, shopping malls, school and colleges, parking lots, roads, etc. to prevent terrorism, theft, accidents and illegal parking, vandalism, fighting, chain snatching, crime and other suspicious activities. It is very difficult to watch public places continuously, therefore an intelligent video surveillance is required that can monitor the human activities in real-time and categorize them as usual and unusual activities; and can generate an alert. Recent decade witnessed a good number of publications in the field of visual surveillance to recognize the abnormal activities. Furthermore, a few surveys can be seen in the literature for the different suspicious activities recognition; but none of them have addressed different suspicious activities in a review. In this paper, we present the state-of-the-art which demonstrates the overall progress of suspicious activity recognition from the surveillance videos in the exam hall. We include a brief introduction of the suspicious human activity recognition with its issues and challenges. This system consists of suspicious activities such as cheating in the exam hall, speaking with other candidates, change in the position. In general, we have discussed all the steps those have been followed to recognize the human activity from the surveillance videos in the literature; such as foreground object extraction, object detection based on tracking or non tracking methods, feature extraction, classification, activity analysis and recognition.

Keywords: Suspicious Activity, Face Detection, Face Recognition, Surveillance video, CNN Convolution Neural Network

Edition: Volume 9 Issue 2, February 2020,

Pages: 910 - 912

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