Individual identification of Holstein dairy cows based on detecting and matching feature points in body images

Abstract

Image processing technology has been used in precision dairy farming to support management decisions. Vision-based animal identification systems can become a potential alternative to RFID. In this paper, a vision system is proposed to extract body images and identify Holstein cows. Side view videos of dairy cattle walking in a straight line were collected. Cow mask was detected using Adaptive SOM method. The largest inscribed rectangle was extracted to locate the cow's body area. A total of 528 videos were collected from 66 cows, and 3 videos were randomly selected for each cow to build template datasets, while the rest of the videos were used as test data. Feature points of the body image were extracted and matched with the template dataset to identify the unknown cow. Four feature extraction methods and two matching methods were investigated and evaluated. The results showed that the highest identification accuracy was 96.72% when the FAST, SIFT and FLANN methods were used for feature extraction, descriptor, and matching, respectively. However, the combination of ORB and BruteForce had better computational efficiency on the basis of high accuracy. Software was implemented and can realise accurate identification of dairy cattle in real-time.