An intelligent method for dairy goat tracking based on Siamese network

Abstract

Tracking livestock can obtain their behavioral information, position information, activity data, and health status. Research on a robust, high-precision, non-contact, and real-time visual object tracking algorithm has important practical value for the management of livestock farming. Since dairy goats are collectively raised livestock, the difficulty in accurately monitoring large-scale goats farms lies in automatically tracking the individual. In this study, a novel Siamese Network Guided by Attention Mechanism (AMTracker) was proposed to track single dairy goat in the real farm scene. First of all, through analysis, we found that there were lots of similar goats raised in the same sheepfold, which led to similarity interference challenge when tracking someone. Secondly, the EfficientNet was employed to map features and the BiFPN model was employed to fuse the features at different levels. Next, we introduced the attention mechanism to improve the correlation between template frame and search frame to deal with the problem of similar target interference. Finally, we used an Anchor-free network to predict the position of the dairy goat in the search frame. The experimental results showed that AMTracker was superior to four state-of-the-art methods in terms of four evaluation indicators, and the Expected Average Overlap (EAO), Robustness (R), Precision (Prec) and Success (Succ) of AMTracker was 0.340, 0.455, 0.835 and 0.657, respectively. The tracker ran in a real-time manner with an average analysis speed of 30 fps. Hence, it was demonstrated that the proposed approach could offer one effective way for automatically tracking a dairy goat in real farms with complex conditions.