Based on tracking algorithm of learning discriminative model prediction for tracking (DIMP),a discriminative single target pedestrian tracking algorithm with adaptive tracking state is proposed to address the problems of unstable tracking state due to background similarities interference,mutual occlusion between pedestrians and background cluter encountered in the pedestrian tracking process.The response map is obtained by the convolution operation of the classification filter and the search region in the tracking process,and the tracking state is divided into weak response state,multi-peak strong response state,and single-peak strong response state by the response map.For the influence of disturbances in the multi-peak strong response state,an online update strategy is proposed to update the classification filter by using the excitation and suppression losses to improve the discriminative ability of the classification filter.For the problem of inaccurate target prediction in multi-peak strong response and weak response states,the target position is corrected by offset and adding candidate frames to improve the tracking accuracy. The proposed algorithm is experimentally verified, which achieves precision of 0.978 and a success rate of 0.740 on pedestrian video sequences with a real-time speed of 30 fps under NVIDIA GTX 1650.