Abstract:

Pedestrian detection is an important part of image processing and computer vision research, and it is usually used in the field of video surveillance and smart vehicles.Pedestrian images are vulnerable to the background,and using common methods such as frame difference and training classifier to detect pedestrian has problems such as low accuracy rate,algorithm complexity and poor realtime pedestrian detection.In this paper we use an improved framedifference method to obtain the pedestrian movement information.Then we utilize histogram coordinates to divide the movement region correspondingly.Finally,the motion area is detected and recognized by training a double feature cascade classifier. Experimental results show that our method can effectively reduce false and missed phenomenon,the average detection time is reduced by 32.77 ms,and the average detection accuracy is increased by more than 10%.The recognition accuracy and speed are improved effectively.

Key words: improved frame difference;Haar feature;Shapelet feature;cascade classifier