Top-down thermal tracking based on rotatable elliptical motion model for intelligent livestock breeding

Abstract

Thermal sensors bring a lot of benefits for object tracking due to the robustness to lighting changes over time, which still give a great difficulty to traditional tracking methods. Despite this powerful advantage, the ambiguity due to background clutters and occlusions makes the problem of thermal sensor-based tracking intractable. In this paper, we propose a novel framework for multiple object tracking in livestock breeding spaces using a single thermal sensor. The key idea of the proposed method is to define a new rotatable elliptical motion model, which is suitable for describing complicated motions of animals. Moreover, the proposed local constraint guides our tracker to robustly chase targets even with severe occlusions. Experimental results on various thermal video sequences show the significant improvement for tracking animals compared to other approaches proposed in the literature.