Research on particle filter tracking algorithm based on multi-feature covariance matrix

Aiming at the problem that the particle filter target tracking based on single feature is vulnerable to environmental impacts and is difficult to deal with the size scaling and rotation changes, this paper proposes a particle filter tracking algorithm based on covariance matrix. The algorithm uses the covariance matrix to fuse multiple features of the target in the framework of particle filter, which improves the adaptability of the algorithm. By introducing the covariance integral graph, the dimension of particle computation is effectively reduced, which reduces the computational cost of the algorithm and improves the real-time performance. The processing efficiency of the algorithm is effectively improved through the mean value calculation in covariance to remove the noise. Simulation experiment results show that the proposed algorithm is robust to the target tracking under the condition of illumination change, environmental disturbance and scale change.