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Open AccessArticle

Long-Term Tracking of Group-Housed Livestock Using Keypoint Detection and MAP Estimation for Individual Animal Identification

1
Department of Electrical and Computer Engineering, University of Nebraska–Lincoln, Lincoln, NE 68505, USA
2
Department of Animal Science, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3670; https://doi.org/10.3390/s20133670
Received: 7 May 2020 / Revised: 8 June 2020 / Accepted: 16 June 2020 / Published: 30 June 2020
(This article belongs to the Section Physical Sensors)
Tracking individual animals in a group setting is a exigent task for computer vision and animal science researchers. When the objective is months of uninterrupted tracking and the targeted animals lack discernible differences in their physical characteristics, this task introduces significant challenges. To address these challenges, a probabilistic tracking-by-detection method is proposed. The tracking method uses, as input, visible keypoints of individual animals provided by a fully-convolutional detector. Individual animals are also equipped with ear tags that are used by a classification network to assign unique identification to instances. The fixed cardinality of the targets is leveraged to create a continuous set of tracks and the forward-backward algorithm is used to assign ear-tag identification probabilities to each detected instance. Tracking achieves real-time performance on consumer-grade hardware, in part because it does not rely on complex, costly, graph-based optimizations. A publicly available, human-annotated dataset is introduced to evaluate tracking performance. This dataset contains 15 half-hour long videos of pigs with various ages/sizes, facility environments, and activity levels. Results demonstrate that the proposed method achieves an average precision and recall greater than 95% across the entire dataset. Analysis of the error events reveals environmental conditions and social interactions that are most likely to cause errors in real-world deployments. View Full-Text
Keywords: precision livestock; multi-object tracking; keypoint detection; activity tracking; long-term tracking; animal behavior; maximum a posteriori classification precision livestock; multi-object tracking; keypoint detection; activity tracking; long-term tracking; animal behavior; maximum a posteriori classification
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T. Psota, E.; Schmidt, T.; Mote, B.; C. Pérez, L. Long-Term Tracking of Group-Housed Livestock Using Keypoint Detection and MAP Estimation for Individual Animal Identification. Sensors 2020, 20, 3670.

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