Tracking Livestock Using a Fully Connected Network and Kalman Filter

Sarwar, Farah; Griffin, Anthony; Pasang, Timotius

Multiple object tracking (MOT) consists of following the trajectories of different objects in a video with either fixed or moving background. In recent years, the use of deep learning for MOT in the videos recorded by unmanned aerial vehicles (UAVs) has introduced more challenges and hence has a lot of room for extensive research. For the tracking-by-detection method, the three main components, object detector, tracker and data associator, play an equally important role and each part should be tuned to the highest efficiency to increase the overall performance. In this paper, the parameter selection of the Kalman filter and Hungarian algorithm for sheep tracking in paddock videos is discussed. An experimental comparison is presented to show that if the detector is already providing good results, a small change in the system can degrade or improve the tracking capabilities of remaining components. The encouraging results provide an important step in an automated UAV-based sheep tracking system.