关键词: 视觉目标追踪, 粒子滤波算法, 协方差矩阵, 特征融合

Abstract: There are the salient features of target nonlinearity and non-Gauss distribution of noise with the vision target recognition method of intelligent vehicle, so it’s hard for existing algorithms to estimate target state in real time. Due to the complexity and changeability of objects needed to be recognized, it’s almost impossible to adopt complete features to describe the target and its dynamic background. A covariance matrix fused with color features and speed-up robust features is proposed in this paper, which is used for particle filtering algorithm, thus achieving the accurate tracking of target. Firstly, the collected image is pretreated to obtain the region of interest. Secondly, a target feature covariance matrix in the ROI is constructed by fusing color features and speed-up robust features. Then, target state prediction model and state observation model used for particle resampling process in improved particle filter algorithm are built, which can implement accurate tracking of targets. Finally, the method is compared with traditional particle filter method characterized by single color features and speed-up robust features. Test results show that, for vision target recognition and tracking of intelligent vehicle when light environment is instantaneous changed, target object has short duration occlusion or target object changes attitude, the accuracy and robustness of the algorithm are effectively improved with the premise of meeting real-time requirement.

Key words: target recognition and tracking, particle filtering algorithm, covariance matrices, feature fusion