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ToxId: an efficient algorithm to solve occlusions when tracking multiple animals.

Alvaro RodriguezHanqing ZhangJonatan KlaminderTomas BrodinMagnus Andersson
Published in: Scientific reports (2017)
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.
Keyphrases
  • machine learning
  • deep learning
  • neural network
  • healthcare
  • high intensity
  • genome wide
  • computed tomography
  • current status
  • magnetic resonance imaging
  • dna methylation
  • genetic diversity