Aiming at the problem that the automobile and pedestrian tracking algorithm can not continue to track because of the pedestrian is sheltered and the high relative speed between automobile and pedestrian

an improved anti-occlusion automobile and pedestrian tracking method is designed and realized

which introduces the histogram of oriented gradient（ HOG） and scale-invariant feature transform（ SIFT） feature on the basis of kernel correlation filter（ KCF） tracking algorithm. When the target pedestrian in the video frame does not appear or partially appears

the SIFT feature and HOG feature of the current frame are extracted. Principal component analysis（ PCA）dimension reduction technology is used to reduce the dimension of multi-feature. Then the fusion feature set of HOG and SIFT is constructed by serial combination

and the feature template HOG-SIFT is generated. By matching the generated feature template with the HOG-SIFT feature in the next frame

pedestrians can be quickly repositioned and tracked. The result of video sequence comparison experiment show that

compared with TLD

CSK and KCF

the tracking accuracy of this algorithm is improved by 24. 1 %

15. 8 % and 2. 2 %

respectively.