A novel method for people and vehicle classification based on Hough line feature

In this paper, we propose a novel and simple method for people and vehicles classification in far distance video surveillance. In this approach, moving objects are firstly segmented from background using a background subtraction technique. Secondly, edges of moving objects are extracted using canny operator. Then straight lines of edges of moving objects are extracted by Hough transform and feature based on Hough line feature (HouLR) for classification is constructed. Finally, moving objects are classified into people or vehicle by HouLR feature. We test our method on several videos in different scenes. The experimental results show that our approach is simple and fast, and has high classification accuracy, not only can distinguish single person from vehicle but also can distinguish group of people from vehicle. The proposed method needs no advance scene calibration, no object tracking and no sample training, which is easy to transplant to other scene.