In correlation filter tracking, occlusion and object scale change can lead to tracking failure easily. To deal with this problem, a correlation filter tracking algorithm based on online detection and scale-adaption is proposed. The target is initially located through a correlation filter tracker fusing histogram features of oriented gradient, color attribute features and illumination invariant features. The reconstruction residual of local sparse representation model is used for occlusion discrimination. If occlusion occurs, online support vector machine detection will be carried out and target relocating will be realized. Scale estimation from coarse to precise is carried out, and precise scale of target is obtained by scale pre-estimation and Newton iterative method. A balanced model updating strategy is used to update correlation filter regularly and update sparse representation model and support vector machine conservatively. Experimental results show that, compared with existing tracking algorithms, the proposed algorithm can effectively reduce the occlusion, target scale change and other complicated factors, and can gain higher distance precision and success rate on 50 groups of test sequences. The overall performance of the proposed algorithm is better than other contrast algorithms.