A Binary Particle Swarm Optimization and Support Vector Machine-based Algorithm for Object Detection
一种基于二值粒子群优化和支持向量机的目标检测算法

This paper proposes a novel object detection method, namely the BPSO-SVM-based detection algorithm that combines Binary Particle Swarm Optimization (BPSO) and Support Vector Machine (SVM) techniques to cope with feature selection issue for object detection under complex scenarios. In the proposed algorithm, object detection is regarded as a two-class categorization problem and feature subset is selected using a wrapper model, in which the BPSO searches the whole feature space and a SVM classifier serves as an evaluator for the goodness of the feature subset selected by the BPSO. Using the proposed BPSO-SVM-based feature selection scheme, feature dimensionality is reduced and classification performance of the SVM classifier is greatly enhanced. Experimental results show the increase on detection accuracy of the proposed algorithm for object detection in complex backgrounds with pose, scale, illumination variations and partial occlusions as well as the significant improvement on detection speed.