Real-time human detection for surveillance applications

Human detection has been an active field of research for years due to its importance in surveillance applications such as video surveillance system for security. Mild accuracy and computational time has been proven for normal methods in features extraction and classification. There is a need to study suitable method to improve on the overall performance of the detection system where having additional function such as describing features of the detected human will further enhance the efficiency. The use of Histogram of Oriented Gradient (HOG) is implemented in the detection of human figure in an image where vectors will be obtained from each pixel in the image through the computation of horizontal and vertical components; these vectors will be used to form a histogram for each image. The histograms will be utilized in the Support Vector Machine (SVM) for classification which is used with the function of sliding window in the detection of human figure in the image. Precision in the detection has been enhanced by having the recursive function to detect the human figure in magnification. The overall accuracy has improved with the enhancement of using additional feature of magnitude inclusive for the HOG computation and further enhanced with the aid of additional features of magnitude inclusive and limitation of magnitude for the HOG computation. Furthermore, segmentation has been performed on the head and body of the human figure for the purpose of system integration with the biometrics’ description. A simulation of the real-time application such as recorded video will be used to test on the integrated system.Bachelor of Engineerin