Aiming at the problem that object tracking is subject to failure in complex scenes such as occlusion and illumination variation, a highaccuracy and robust object tracking algorithm is proposed. Firstly, the target model based on edge information, the filter model based on HOG feature and the color model based on color histogram are merged into a more accurate and strong robust tracking model. Then, the double tracking reliability judgment criterion based on the score of the feature is proposed to detect the reliability of the tracking result. Finally, when the reliability of the tracking result is low, particle filtering, sparse representation and distance constraint positioning are used for redetection to achieve continuous and stable tracking. On the OTB2015 dataset, the average overlap precision of the proposed algorithm is 782%, the average center location error is 231 pixel and the average tracking rate is 308 f/s, which indicates that the accuracy and robustness are better than those of other algorithms. The algorithm was verified on mobile robot and vehicle tracking platform, the average overlap precisions are 975% and 972%, the average center location errors are 68 pixel and 126 pixel, respectively, and the average tracking rates are 291 and 284 f/s, respectively. The proposed algorithm can effectively track the targets in above mentioned complex scenes and meet the realtime requirements.