Abstract: To solve the problem of particle filter's degradation and lower tracking robustness with single feature application, a kernel particle filter tracking method was proposed by multi-feature fusion. Firstly, the method of new weight updating in kernel particle filter was put forward. Then the robust tracking was achieved by integrating the color and texture feature under the framework of kernel particle filter method. Spatiograms and integral histogram was used respectively to calculate color and texture feature. The disadvantages of their own were effectively overcome by the two kinds of calculation methods for two characteristics. The sampling efficiency was improved by using the algorithm and the larger calculation problem of particle filter and particle degradation was solved. Finally target tracking experiment was conducted by adopting the methods for complex background and serious occlusion circumstances. Experimental results show that the proposed algorithm can track target accurately and may well deal with object occlusion.