Aiming at the low tracking accuracy of target work-piece in complex environment, an improved tracking method for target workpiece based on ECO is presented. Firstly, based on the framework of eco correlation filter, VGG features and traditional manual features are weighted and fused to improve the tracking accuracy. Then, the fast discriminant scale space tracker is used to track the target workpiece adaptively. Finally, a high confidence update index is introduced to determine the sparse update strategy of the tracking model to improve the robustness of the algorithm. Tested on the OTB - 2015 standard dataset and compared with other mainstream tracking algorithms, the experimental results show that the average tracking accuracy and the average overlap accuracy of the algorithm are both optimal, reaching 89. 2% and 68. 6%, This algorithm also has good tracking performance for target work-piece dataset taken with CCD industrial camera, which further verifies the validity of the algorithm.