Multi-target track based on mixtures of particle filtering

For the problem of detecting and tracking a varying number of dim small target in IR image sequences, multitarget track-before-detect approach based on mixture models of probability densities is proposed and mixtures of t distribution particle filters (MTPF) are developed for the implementation of the proposed approach in this paper. The existence of each tracked target is detected by using the sequential likelihood ratio test estimated by the output of component particle filter. New targets are detected by the appearance probabilities in the discrete occupancy grid in the image frame. The algorithm explicitly handles the instantiation and removal of filters in case new objects enter the scene or previously tracked objects are removed. The proposed approach overcomes the curse of dimensionality by estimating each target state independently by using separate particle filter and avoids the exponential increase in the estimation complexity. Simulation experiments illustrated that the MTPF algorithm can detect and track the variable number of dim small targets in the IR images, and simultaneously detect the disappearance and appearance of targets.