Authors:
Matthew van der Zwan
and
Alexandru Telea
Affiliation:
University of Groningen, Netherlands
Keyword(s):
Time-of-Flight Cameras, Video Tracking, Vision for Robotics, Automatic Milking Devices.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Robotics
;
Software Engineering
;
Tracking and Visual Navigation
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.