A multi-filter collaborative tracking algorithm based on high-confidence updating strategy is proposed. First, the multi-layer convolutional features of the region around the target are extracted using VGG-Net-19, which is a convolutional network architecture, followed by an adaptive feature fusion strategy with the designed deep filter to get the initial position of the target. Meanwhile, a scale filter is constructed to detect the size change of the target. Then, a tracking confidence indicator named primary and secondary peak slope ratio is utilized, which helps to build a high-confidence model updating strategy. Finally, when the confidence is insufficient, the object region proposals are extracted by EdgeBox method, and the final position of the target is determined by the designed re-detection filter. The experimental results on OTB-100 and TC-128 datasets show that the proposed algorithm achieves high tracking precision and also tracks steadily under some complex circumstances, such as occlusion, illumination variation, and out-of-view.