Pedestrian identification based on fusion of multiple features and multiple classifiers

Abstract

Finding a specific person from videos in surveillance systems is a challenging task. In the videos, different people cannot be the same in a whole body appearance. Based on this fact, this paper has proposed new methods based on fusion of textures, angle histograms and color moments to find a specific person. The human visual system can discriminate different objects quickly and efficiently. Inspired by on-center and off-center receptive fields in the visual system, a network model based on spiking neurons is proposed to extract texture features, and it has behaviors similar to Gabor filters. According to human body proportion, a person image is divided into three parts: head, torso and leg. Texture features of three parts are extracted by means of this network. Back propagation neural network, multi-class SVM and KNN are used as classifiers. For improving recognition rate, different fusion methods have been studied such as the fusion of texture features and other features in three body parts, and decision fusion using voting mechanism, probability combination etc. The experimental results for different methods are provided and the best fusion method is proposed. The technology of Compute Unified Device Architecture is applied in the experiments, which greatly reduces the running time for extraction of texture features.