To solve the problem of poor robustness and low accuracy of workpiece tracking in complex industrial production environment,this paper presents a multi-attention fusion workpiece tracking algorithm based on accurate tracking by overlap maximization (ATOM).The algorithm uses ResNet50 as the backbone network,first incorporating a multi-attention mechanism,which makes the network pay more attention to the key information of the target workpiece.Secondly,the attention feature fusion (AFF) module is used to fuse the deep and shallow features to better preserve the semantics and details of the target workpiece in order to adapt to the complex and changeable environment of industrial production.Finally,the third and fourth layers features of the backbone network are fed into the CSR-DCF classifier,and the resulting response graphs are fused to obtain rough locations of target workpieces and accurate target frames through the state estimation network.Experiments show that the Success and Precision of the algorithm on OTB-2015 dataset are 67.9% and 85.2%,respectively.The overall score on VOT-2018 dataset is 0.434,which has high accuracy and robustness.On the target workpiece sequence taken by the CCD industrial camera,the algorithm is further validated to meet the common challenges efficiently in the workpiece tracking process.