A Robust student's-t distribution PHD filter with OCSVM updating for multiple human tracking

We propose a novel robust probability hypothesis density (PHD) filter for multiple target tracking in an enclosed environment, where a one-class support vector machine (OCSVM) is used in the update step for combining different human features to mitigate the effect of measurement noise on the calculation of particle weights. A Student's-t distribution is employed to improve the robustness of the filters whose tail is heavier than the Gaussian distribution and thus has the potential to cover more widely-spread particles. The OCSVM is trained based on both colour and oriented gradient (HOG) histogram features and then used to mitigate the measurement noise from the particle selection step, thereby improve the tracking performance. To evaluate the proposed PHD filter, we employed two sequences from the CAVIAR dataset and used the optimal subpattern assignment (OSPA) method as an objective measure. The results show that the proposed robust PHD filter outperforms the traditional PHD filter.