Multi-feature fusion tracking based on new nonlinear filtering
一种新型非线性滤波的多特征融合跟踪算法

This paper proposed a new kind of nonlinear filtering for the state estimation of nonlinear systems. The proposed algorithm based on quadrature Kalman filter by using integral pruning factor, which optimized and reorganized the integration point. New algorithm overcame the particle degeneration phenomenon well. In the improving particle filter framework, this algorithm used color and motion edge character as observation model, and fused feature weights through the D-S evidence theory. The proposed method effectively avoided bad robust questions rosed by the single color feature in the posture change and similar feature occlusion. Experiment results indicate that the proposed method is more robust to track object in complex scene and the tracking precision ascends nearly 32%.