IJFANS International Journal of Food and Nutritional Sciences

ISSN PRINT 2319 1775 Online 2320-7876

DEEP LEARNING APPROACH FOR SUSPICIOUS ACTIVITY DETECTION FROM SURVEILLANCE VIDEO

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D Suman,E Madhu Goud,N Shiva Kumar,G Nikhil Kumar

Abstract

The importance of video surveillance in modern society cannot be overstated. Once AI, ML, and DL were introduced, the technologies were already too far forward. Utilizing the aforementioned permutations, several methods have been developed to discern between different types of suspicious behaviour based on the live monitoring of film. Human conduct is the most erratic, and it's sometimes hard to tell whether an unusual pattern of behaviour is really typical. An alarm message is sent to the appropriate authority if the system predicts suspect behaviour in a school setting, and if the activity is normal, no alert is sent. In many monitoring applications, a series of frames taken from a video are used in rapid succession to conduct checks. The whole structure may be broken down into two halves. First, a classifier uses video frame computations to acquire features; next, using those characteristics, a prediction of whether a frame is suspicious or not may be made

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