Multi-Center Agent Loss for Visual Identification of Chinese Simmental in the Wild

Simple Summary Visual identification of cattle in a realistic farming environment is helpful for real-time cattle monitoring. Based on continuous cattle detection, identification, and behavior recognition, it is possible to utilize cameras on farms within company or government networks to provide the services of production supervision, early disease detection, and animal science research for precision livestock farming. However, cattle identification in the wild is still a difficult problem due to the high similarities of different identities and the variances of the same identity as posture or perspective changes. Our proposed method based on deep convolutional neural networks and deep metric learning provides a promising approach for cattle identification and paves the way toward continuous monitoring of cattle in a nearly natural state. Visual identification of cattle in the wild provides an essential way for real-time cattle monitoring applicable to precision livestock farming. Chinese Simmental exhibit a yellow or brown coat with individually characteristic white stripes or spots, which makes a biometric identifier for identification possible. This work employed the observable biometric characteristics to perform cattle identification with an image from any viewpoint. We propose multi-center agent loss to jointly supervise the learning of DCNNs by SoftMax with multiple centers and the agent triplet. We reformulated SoftMax with multiple centers to reduce intra-class variance by offering more centers for feature clustering. Then, we utilized the agent triplet, which consisted of the features and the agents, to enforce separation among different classes. As there are no datasets for the identification of cattle with multi-view images, we created CNSID100, consisting of 11,635 images from 100 Chinese Simmental identities. Our proposed loss was comprehensively compared with several well-known losses on CNSID100 and OpenCows2020 and analyzed in an engineering application in the farming environment. It was encouraging to find that our approach outperformed the state-of-the-art models on the datasets above. The engineering application demonstrated that our pipeline with detection and recognition is promising for continuous cattle identification in real livestock farming scenarios.