Abstract: Appearance and scale change are the difficulties of pedestrian tracking. To solve the problem of multi-scale pedestrian tracking is the key factor to enhance the practicability of the algorithm. On the basis of KCF(kernel correlation filter) algorithm，this paper uses multiple correlation filters(such as head and hip)to assist the tracking of the body trunk filter. The distance change rate which is obtained by comparing the distance between the pedestrian’s head and hip of every image frame(except the first frame)with the initial frame is used to zoom the search area， so as to avoid inaccurate target location and time waste. By adjusting the size of the target’s bounding box， the problem of target’s template shift caused by gradual change of the target template including background features or local features is solved. The experiment is conducted on eighteen pedestrian scene video sequences with obvious scale changes in VOT2016 dataset， and the experimental results show that the algorithm proposed has higher tracking accuracy.

Key words: pedestrian multi-scale tracking, correlation filters mutually assisted, KCF(kernel correlation filter), search range, tracking accuracy