Approximating Inference on Complex Motion Models using Multi-Model Particle Filter

Abstract. Due to its great ability of conquering clutters, which is es-pecially useful for high-dimensional tracking problems, particle filter be-comes popular in the visual tracking community. One remained difficulty of applying the particle filter to high-dimensional tracking problems is how to propagate particles efficiently considering complex motions of the target. In this paper, we propose the idea of approximating the complex motion model using a set of simple motion models to deal with the track-ing problems cumbered by complex motions. Then, we provide a practical way to do inference on the set of simple motion models instead of original complex motion model in the particle filter. This new variation of parti-cle filter is termed as Multi-Model Particle Filter (MMPF). We apply our proposed MMPF to the problem of head motion tracking. Note that the defined head motions include both rigid motions and non-rigid motions. Experiments show that, when compared with the standard particle filter, the MMPF works well for this high-dimensional tracking problem with reasonable computational cost. In addition, the MMPF may provide a possible solution to other high-dimensional sequential state estimation problems such as human body pose estimation and sign language esti-mation and recognition from video.