A multi-target tracking algorithm using texture for real-time surveillance

Abstract

In this paper, we present a texture-based multitarget tracking algorithm. Moving objects are described by local binary patterns (LBP), which is a kind of discriminative texture descriptor. The Kalman filter is introduced into the algorithm to predict the blob's new position and size. Blobs are searched in the neighborhood of the Kalman predictions. If more than one are found, the LBP distance, which has been evaluated valid for blob distinguishing in our experiments, is applied to locate the tracking target. Cooperates with the LBP distance, the Kalman filter is efficient in dealing with collisions. Tracking results demonstrate the effectiveness of the algorithm. This algorithm has been implemented on PC and DSP platforms and achieved real-time performance.