Automatic Detection and Tracking of Moving Object Employing a Particle Filter

We proposed a method for automatic detection and tracking of moving object employing a particle filter in conjunction with a color feature method. The particle filtering is used because it is robust for non-linear and non-Gaussian dynamic state estimation problems and performs well when clutter and occlusions are present on the image. A histogram-based framework is used to describe the color feature of the target object. Bhattacharyya distance is used to measure the similarity between each sample's histogram with a specified target model. The target model update is performed to obtain the best match to the target model. The method is capable to detect and track successfully the moving object in different outdoor environment based on variance of the samples and an appearance condition. The experimental results and data show the feasibility and the effectiveness of our method.