Robust and Fast Teat Detection and Tracking in Low-resolution Videos for Automatic Milking Devices

Abstract

We present a system for detection and tracking of cow teats, as part of the construction of automatic milking devices (AMDs) in the dairy industry. We detail algorithmic solutions for the robust detection and tracking of teat tips in low-resolution video streams produced by embedded time-of-flight cameras, using a combination of depth images and point-cloud data. We present a visual analysis tool for the validation and optimization of the proposed techniques. Compared to existing state-of-the-art solutions, our method can robustly handle occlusions, variable poses, and geometries of the tracked shape, and yields a correct tracking rate for over 90% for tests involving real-world images obtained from an industrial AMD robot.