Authors:Payal Bose Prof. Samir K. Bandhopadhyay Abstract:Detecting human and human motion through a video surveillance sys-tem is an important task. It helps in various fields, like crowd analysis, identifying abnormalities in human behavior in public places, identifying a person and their gender, etc. To detect a human and its motion in a video the first step is to detect the moving object correctly. Background subtraction method is an efficient way to detect the moving object efficiently on the foreground frame. Object detection and extraction could be performed in various ways. Once the object is found then a classification method is applied to recognize the object and its motion. In this paper, first the background subtraction method is applied to detect the person in a video input file. Then this method is applied to track the human’s motion through the entire video. In the second step, the Histogram Oriented Gradient Method (HOG) is used to extract the features from the input video file. And in the last step Support Vector Machine (SVM) is used to classify the detected per-son’s identity and its motion. In this experiment two different multi-class classi-fiers are used, SVM and Decision-Tree (DT) to compare the performance of the classification models. Finally, a detailed comparison of each person and the mo-tion class is performed to compare the classification rate of the two classifiers.
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