Discrimination of Pedestrian Images Using Shape Features and Neural Networks

抄録

Intelligent surveillance systems capable of discriminating pedestrians in the street are one of the main application areas of computer vision. This paper proposes a method to discriminate pedestrian images into several classes by using pedestrian shape features and artificial neural networks. To overcome the difficulty of pedestrian identification due to shape variation over time, several video-image processing and intelligent discrimination methods were adopted and developed. At the front end, image and video processing was performed to separate the background from the foreground images. The pedestrian shape features were extracted by Fourier transform, and then feed-forward neural networks with back-propagation learning algorithms were employed to discriminate among several classes of the moving pedestrian images, i.e., pedestrian, cyclist, or other non-pedestrian objects. The experimental results demonstrated the capability of the proposed system to discriminate pedestrians in a real life pedestrian environment. On average, discrimination accuracy was achieved in 82% and 87% using the complex number and the centroid distance function method, respectively.