A Novel Color Based Particle Filter Algorithm for Object Tracking

The traditional histogram based particle filter often has to compromise between accurate representation of color distribution and computational efficiency,which affects the performance of the tracking algorithm or even results in tracking failures.To address this problem,the paper presents a novel color based particle filter algorithm for object tracking.The proposed algorithm utilizes a model based on adaptive partition of color space,which can represent accurately the color distribution of the object with smaller number of subspaces.The paper proposes extended integral images,by which the pixel number,mean vector and covariance matrix of each sub-space can be obtained in simple array read operations that results in fast computation of the color model.The construction of the proposed integral images on CPU is,however,time-consuming,thus this paper proposes a GPU based parallel algorithm for fast computation of the integral images.The parallel algorithm consists of three thread grids respectively executing three Kernel functions with GPU on the video card,which sequentially builds the raw integral images,performs prefix sum with respect to rows and then with respect to columns of the original integral images.Compared to the traditional histogram based particle filter algorithm,the proposed one has much shorter tracking time,and in the meantime,attains improved tracking accuracy and robustness.