This paper presents a relative discriminative histogram of oriented gradients (HOG) (RDHOG)-based particle filter (RDHOGPF) approach to traffic surveillance with occlusion handling. Based on the conventional HOG, an extension known as RDHOG is proposed, which enhances the descriptive ability of the central block and the surrounding blocks. RDHOGPF can be used to predict and update the positions of vehicles in continuous video sequences. RDHOG was integrated with the particle filter framework in order to improve the tracking robustness and accuracy. To resolve multiobject tracking problems, a partial occlusion handling approach is addressed, based on the reduction of the particle weights within the occluded region. Using the proposed procedure, the predicted trajectory is closer to that of the real rigid body. The proposed RDHOGPF can determine the target by using the feature descriptor correctly, and it overcomes the drift problem by updating in low-contrast and very bright situations. An empirical evaluation is performed inside a tunnel and on a real road. The test videos include low viewing angles in the tunnel, low-contrast and bright situations, and partial and full occlusions. The experimental results demonstrate that the detection ratio and precision of RDHOGPF both exceed 90%.