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Detection of Suspicious Human Activity based on CNN-DBNN Algorithm for Video Surveillance Applications

Detection of suspicious human actions in automated video surveillance applications, is of great practical importance. Those kind of unusual activities in human is very difficult to acquire and classify to predict. In our proposed work, automatic tracking and detecting unusual movement’s problems in closed circuit videos was resolved. Firstly, the videos are converted into frames. Then from the obtained frames, humans are detected from the video using a background subtraction method. Then the features are extracted using a convolutional neural network (CNN). The features thus extracted are fed to a Discriminative Deep Belief Network (DDBN). Labeled videos of some suspicious activities are also fed to the DDBN and their features are also extracted. Then the features extracted using Convolutional Neural Network (CNN) are compared against these features extracted from the labeled sample video of classified suspicious actions using a Discriminative Deep Belief Network (DDBN) and various suspicious activities are detected from the given video and results shows increase accuracy of 90% for the proposed framework for classification.