Abstract: The behavior of sheep can reflect its health status and physiological stages. Automatic tracking of the objects for sheep in the farm environment is a prerequisite for statistics and analysis of its behavior. In this paper, captive sheep were used as the experimental subjects, and then a multiple object tracking method for sheep based on StrongSORT algorithm was proposed. It used YOLOv5-CBAM as the front-end detector, then combined the currently advanced StrongSORT tracker.The experimental results showed that, in the short video tracking, the multiple object tracking accuracy, multiple object tracking precision, the total number of identity switches and IDF1 of 10 sheep reached 91. 6%, 0. 269, 52 and 70. 7%, respectively.Compared with the YOLOv5+StrongSORT algorithm, the multi-object tracking accuracy of the YOLOv5-CBAM+StrongSORT algorithm proposed in this paper was improved by 0. 4%, the multi-object precision tracking was basically unchanged, the number of identity switching times was reduced by 17. 5%, and the IDF1 value was increased by 3. 2%. In the long video tracking, the above evaluation indicators were 57. 3%, 0. 244, 21 and 47. 9%, respectively, and the advantages of YOLOv5-CBAM+StrongSORT were mainly reflected in the number of identity switches, which were reduced by 13, 10 and 12 times compared with YOLOv5+ByteTrack, YOLOv5+DeepSORT, and YOLOv5+OCSORT, respectively.