Hierarchical Kalman-particle filter(HKPF) is successfully applied to target tracking with adaption to motion changes. However, it only focuses on the optimization of the target position rather than other affine parameters, resulting in many particles needed to find the optimal state. To achieve fast tracking in complex environment, self-tuning strategy-based hierarchical Kalman-particle filter was proposed to solve the problem. The proposed algorithm marginalized out the linear states in the dynamics to reduce the state dimension, and then found the optimal nonlinear states in a chainlike way with a very small number of particles. The detail process of our algorithm was as follows: first, a local region was estimated by KF; second, self-tuning strategy was used to incrementally generate particles in this region, and an online-learned pose estimator(PE) was introduced to iteratively tune them along the optimal directions according to observations. The comparison among the proposed algorithm and the existing tracking algorithms with real video sequences was implemented, in which the target undergo rapid and erratic motion, or/and dramatic pose change. The results demonstrate that the proposed tracking algorithm can achieve great robustness and very high accuracy with only a very small number of particles.