Persistent animal identification leveraging non-visual markers
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Published:2023-07
Issue:4
Volume:34
Page:
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ISSN:0932-8092
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Container-title:Machine Vision and Applications
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language:en
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Short-container-title:Machine Vision and Applications
Author:
Camilleri Michael P. J.ORCID, Zhang Li, Bains Rasneer S., Zisserman Andrew, Williams Christopher K. I.
Abstract
AbstractOur objective is to locate and provide a unique identifier for each mouse in a cluttered home-cage environment through time, as a precursor to automated behaviour recognition for biological research. This is a very challenging problem due to (i) the lack of distinguishing visual features for each mouse, and (ii) the close confines of the scene with constant occlusion, making standard visual tracking approaches unusable. However, a coarse estimate of each mouse’s location is available from a unique RFID implant, so there is the potential to optimally combine information from (weak) tracking with coarse information on identity. To achieve our objective, we make the following key contributions: (a) the formulation of the object identification problem as an assignment problem (solved using Integer Linear Programming), (b) a novel probabilistic model of the affinity between tracklets and RFID data, and (c) a curated dataset with per-frame BB and regularly spaced ground-truth annotations for evaluating the models. The latter is a crucial part of the model, as it provides a principled probabilistic treatment of object detections given coarse localisation. Our approach achieves 77% accuracy on this animal identification problem, and is able to reject spurious detections when the animals are hidden.
Funder
Engineering and Physical Sciences Research Council National Natural Science Foundation of China Medical Research Council
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Software
Reference62 articles.
1. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV) 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y 2. Silva, J., Lau, N., Rodrigues, J., Azevedo, J.L., Neves, A.J.R.: Sensor and information fusion applied to a robotic soccer team. In: Baltes, J., Lagoudakis, M.G., Naruse, T., Ghidary, S.S. (eds.) RoboCup 2009: Robot Soccer World Cup XIII, pp. 366–377. Springer, Berlin (2010) 3. Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple online and realtime tracking. In: Proceedings—International Conference on Image Processing, ICIP, vol. 2016-Augus, pp. 3464–3468. IEEE Computer Society (2016). https://doi.org/10.1109/ICIP.2016.7533003 4. Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Heckbert, P.S. (ed.) Graphics Gems, pp. 474–485. Academic Press, Cambridge (1994) 5. Brown, S.D.M., Moore, M.W.: The International Mouse Phenotyping Consortium: past and future perspectives on mouse phenotyping. Mamm. genome 23(9–10), 632–640 (2012). https://doi.org/10.1007/s00335-012-9427-x
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