Abstract

Video analysis of animal behaviour is widely used in fields such as ecology, ecotoxicology, and evolutionary research. However, when tracking multiple animals, occlusion and crossing are problematic, especially when the identity of each individual needs to be preserved. We present a new algorithm, ToxId, which preserves the identity of multiple animals by linking trajectory segments using their intensity histogram and Hu-moments. We verify the performance and accuracy of our algorithm using video sequences with different animals and experimental conditions. The results show that our algorithm achieves state-of-the-art accuracy using an efficient approach without the need of learning processes, complex feature maps or knowledge of the animal shape. ToxId is also computationally efficient, has low memory requirements, and operates without accessing future or past frames.

Published in

Scientific Reports
2017, Volume: 7, article number: 14774
Publisher: NATURE PUBLISHING GROUP

UKÄ Subject classification

Computer Vision and Robotics (Autonomous Systems)
Zoology


Publication identifier

DOI: https://doi.org/10.1038/s41598-017-15104-2

Permanent link to this page (URI)

https://res.slu.se/id/publ/119854