The authors propose an effective algorithm for multiple pedestrians tracking, which is constructed in the framework of particle filtering, and it is based on the combination of online boosting tracker and the histogram of oriented gradient (HOG) descriptor for human detection. The combination for the detector and tracker lies on following aspects. First, each detection result is associated to a tracker implemented by the online boosting, which gives the authors scheme robustness for multiple similar objects and then, the output of support vector machine classifier based on HOG is dynamically fused as a component in the observation metric in particle filtering, which makes the tracker more accurate in some difficult conditions. Finally, the states of some particles are replaced by the state given by the detector, so that the tracker can recover from failure quickly. Experiments show the effectiveness of their scheme.