Particle Filter Algorithm for Head Tracking Based on Multi-observation Models
基于多观测模型的粒子滤波头部跟踪算法

Particle filtering has drawn more attention recently due to its superior performance in nonlinear and non-Gaussian problems, which uses an observation model to describe the interested target and its performance depends strongly on the observation model. A novel particle filter algorithm has been proposed for head tracking, which focuses on finding a solution to the problem that the feature of the tracked object does not always match the particle observation model with changing time. In brief, it unifies the difference of the tracked object features with the particle observation models, and thus it forms a group of particles with different models to track the target. Based on the changing displays in the feature cues of the head with the rotation, particles with different observation models convert alternately in the tracking process. To evaluate the novel head tracker performance, some real sequences is tested and some results are shown that the new tracker is robust to the rotation of head in a cluttered background and has better tracking precision than the standard particle filter.