关键词: 运动特征, 位置估计, ACF特征, Real Adaboost

Abstract: According to the characteristics of the movement of pedestrians and the correspondence between position and height of pedestrians in images, the pedestrian detection algorithm combining motion feature with location estimation is proposed. The motion feature and the Aggregated Channel Features（ACF） are extracted, then these features are trained by the classifier, and an evaluation model is built on the possible position of pedestrians. In the detection stage, the candidate pedestrian areas are determined by the classifier, and then the non maximum suppression algorithm is used to eliminate redundant windows. Finally, the model of position evaluation is used to judge the pedestrian candidate regions to eliminate non-target pedestrian.  In the experiment, the Caltech data set is used in the simulation of the algorithm, the results show that our algorithm achieves a Log-Average Miss Rate（LAMR） of 27.12%. Compared with the ACF algorithm, LAMR is decreased by 5.9%. The results of the experiment testify that motion feature can be used as a complementation of static feature, and location estimation of pedestrian has certain ability to distinguish when the camera is fixed.

Key words: motion feature, location estimation, ACF feature, Real Adaboost