Human Detection and Tracking for Video Surveillance A Cognitive Science Approach
This addresses the problem of automating video surveillance for security applications, but it is incremental as it builds on existing techniques.
The paper tackles human detection and tracking in video surveillance by developing a method combining Histograms of Oriented Gradients, Visual Saliency theory, and Deep Multi Level Network, achieving a detection precision of 83.11% and recall of 41.27% with a speed improvement of 76.866 times faster than normal image classification.
With crimes on the rise all around the world, video surveillance is becoming more important day by day. Due to the lack of human resources to monitor this increasing number of cameras manually new computer vision algorithms to perform lower and higher level tasks are being developed. We have developed a new method incorporating the most acclaimed Histograms of Oriented Gradients the theory of Visual Saliency and the saliency prediction model Deep Multi Level Network to detect human beings in video sequences. Furthermore we implemented the k Means algorithm to cluster the HOG feature vectors of the positively detected windows and determined the path followed by a person in the video. We achieved a detection precision of 83.11% and a recall of 41.27%. We obtained these results 76.866 times faster than classification on normal images.