Achievement of multi-target tracking of sheep using deep learning
The movement status of the sheep can reflect its health status,and tracking the movement trajectory of the target sheep in the farm environment is a key step to obtain its movement status.Automated tracking of a small number of targets is practical in specific situations,such as before farrowing or during disease treatment.In order to improve the accuracy of multi-target tracking,an improved DeepSORT-R multi-object tracking algorithm was proposed.In the target recognition stage,the YOLOV5-CBAM network with added attention mechanism was used to realize the detection of sheep targets.In the re-identification stage,the ResNet50 network with added attention mechanism was used to realize the identification of sheep identity.The experimental results of the proposed method for the test set showed that MOTA,MOTP and IDSW reached 76.15%,0.208 and 7.5,respectively.In addition,the proposed method was better in the long video tracking test than the commonly used YOLOV4+DeepSORT and ByteTrack in the scores of the evaluation indicators MOTA,MOTP and IDSW.The experimental results showed that the proposed method can be used for the tracking of multi-target sheep in the actual breeding environment.
NETL
NSTL
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