Video-based Detection and Tracking with Improved Re-Identification Association for Pigs and Laying Hens in Farms

It is important to detect negative behavior of animals for breeding in order to improve their health and welfare. In this work, AI is employed to assist individual animal detection and tracking, which enables the future analysis of behavior for individual animals. The study involves animal groups of pigs and laying hens. First, two state-of-the-art deep learning-based Multi-Object Tracking (MOT) methods are investigated, namely Joint Detection and Embedding (JDE) and FairMOT. Both models detect and track individual animals automatically and continuously. Second, a weighted association algorithm is proposed, which is feasible for both MOT methods to optimize the object re-identification (re-ID), thereby improving the tracking performance. The proposed methods are evaluated on manually annotated datasets. The best tracking performance on pigs is obtained by FairMOT with the weighted association, resulting in an IDF1 of 90.3%, MOTA of 90.8%, MOTP of 83.7%, number of identity switches of 14, and an execution rate of 20.48 fps. For the laying hens, FairMOT with the weighted association also achieves the best tracking performance, with an IDF1 of 88.8%. MOTA of 86.8%. MOTP of 72.8%, number of identity switches of 2, and an execution rate of 21.01 fps. These results show a promising high accuracy and robustness for the individual animal tracking.

Automated summary

In this study, AI is used to assist individual animal detection and tracking for future behavior analysis. Two state-of-the-art deep learning-based Multi-Object Tracking (MOT) methods are investigated, and a weighted association algorithm is proposed to optimize tracking performance. Results show promising high accuracy and robustness for individual animal tracking in both pig and laying hen groups.