A Human Recognition System for Pedestrian Crosswalk.

The concept of pedestrian priority has been taken gradually in recent years. However, there are still many vehicles and motorcycles caused traffic accidents due to ignoring the pedestrian right. In this work, we design and implement a human recognition system based on image processing techniques for pedestrians crosswalk. This system can be used to improve the safety level and reduce the probability of intersection accidents. We use the environment feature vectors obtained by the system to detect the zebra-crossing and find out the range of the zebra-crossing. We propose a dual-camera mechanism to maintain the detection accuracy and improve the fault tolerance of the proposed system. We design an Enhanced-Motion-HOG classification scheme to recognize pedestrians at the road intersection. To verify the feasibility and efficiency of our system, we implement a prototype system and compared the accuracy of the pedestrian detection scheme with the original HOG method and the motion detection method. Experimental results demonstrate that our scheme outperforms these schemes in terms of detection accuracy and processing time.