HUMAN ACTIVITY RECOGNITION SYSTEM (HARS) USING HISTOGRAM OF NORMALIZED FOURIER DESCRIPTOR

This paper presents a system, which is able to recognize different continuous human activities in real time from videos using a single stationary camera. The proposed system has a motion descriptor, Called histogram of normalized Fourier descriptor of object contour. After image segmentation the proposed system extract the object contour and estimate the FD ”Fourier descriptor” of that contour then normalize FD to cancel the effect of starting point variation ,Rotation and Scale. The histogram of the normalized Fourier descriptor is used as a feature vector.The authors used three methods for classification Support Vector Machine,One versus all support vector machine and Naive Bayes classifier. The classification by using SVM shows better performance than other methods. Experimental results on two data sets weizman and KTH validate the proposed system reliability and efficiency.