An effective crossing cyclist detection on a moving vehicle

Vision based cyclist detection is a new application in the field of intelligent transportation. Compared with pedestrian detection, this new problem is more challenging because various appearance and motion of bicycles increase the diversity of the detection objects; therefore existing pedestrian detection approaches can hardly get good overall performance because cyclist detection requires more information represented by more effective features to enable detection. For general object detection and pedestrian detection, histogram of oriented gradient (HOG) features achieved great success; however it have two major drawbacks: time-consuming caused by dense/overlap sampling and only local information is retained. In this paper, we proposed a more effective feature extraction method (i.e., HOG-LP) to overcome the drawbacks of general HOG feature extraction for crossing cyclist detection. On one hand, an improved light/non-overlap sampling method is proposed to speed up HOG feature extraction; on the other hand, pyramid sampling is utilized to extract additional global features in different scale spaces in order to retain more information for high classification accuracy. With efficient feature extraction, a linear SVM classifier is used to further increase the detection speed. The experimental results tested on urban traffic videos show the effectiveness of the proposed method on crossing cyclist detection.