Method to improve efficiency of human detection using scalemap

An efficient method is introduced for detecting humans in surveillance video. The method improves the performance of multiscale human detection through the use of a scalemap, and does not require knowledge of the camera parameters or the use of additional devices. A scalemap is a map that links each position in the observed image to the optimal detection scale. The proposed method efficiently reduces the computational costs by estimating the scale of interest and the region of interest based on the scalemap, while maintaining the accuracy of the detection. It is experimentally shown through an experiment that the proposed method can improve both the accuracy and the efficiency of real-world surveillance videos.

[1]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Ian D. Reid,et al.  Stable multi-target tracking in real-time surveillance video , 2011, CVPR 2011.