As smart mobile devices become increasingly popular，mobile applications such as object detection in videos are greatly limited by the computing and storage capacity of mobile devices． Traditional cloud computing can not meet the requirements of applications for network delay，network jitter，security and other issues． For this purpose，this paper designed the quality of experience ( QoE) as a comprehensive measure of object detection accuracy and energy consumption based on end-cloud collaborative system． Regarding the quality of experience as the optimization goal，this paper then proposed an adaptive scheduling algorithm for video image detection and tracking． The algorithm schedules object detection and object tracking by predicting network bandwidth and calculating transmission delay． Experimental results on the KITTI video dataset show that the detection-tracking adaptive scheduling algorithm can achieve a higher QoE value，significantly reduce the energy loss，and achieve an detection accuracy of 78. 3% ．