HUMAN SUSPICIOUS ACTIVITY DETECTION SYSTEM USING CNN MODEL FOR VIDEO SURVEILLANCE

Computer Engineering

This paper brings forward one amongst the foremost significant applications of human suspicious activity recognition that is termed as anomaly detection. A key concern of any society today is providing safety to an individual. The main reason behind this concern is due to the constantly increasing activities causing threats, starting from deliberate ferocity to an injury caused through an accident. Simple installation of a traditional closed circuit television(CCTV) is not sufficient as it requires a person to continuously stay alert and monitor the cameras, which is quite inefficient. This call for the requirement to develop an security system which is fully automated system that recognizes anomalous activities in real time and brings instant help to the victims. Hence we proposed a system which will examine and detect the suspicious human action from real-time CCTV footage with help of machine learning techniques and generates the alert if the abnormal activity is occurred. The method is implemented on the dataset containing both normal and anomaly activity and experiment has shown better results.

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