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http://dx.doi.org/10.3837/tiis.2019.04.023

Real-Time Cattle Action Recognition for Estrus Detection  

Heo, Eui-Ju (Interdisciplinary Program on Creative Engineering, KOREATECH)
Ahn, Sung-Jin (Interdisciplinary Program on Creative Engineering, KOREATECH)
Choi, Kang-Sun (Interdisciplinary Program on Creative Engineering, KOREATECH)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.4, 2019 , pp. 2148-2161 More about this Journal
Abstract
In this paper, we present a real-time cattle action recognition algorithm to detect the estrus phase of cattle from a live video stream. In order to classify cattle movement, specifically, to detect the mounting action, the most observable sign of the estrus phase, a simple yet effective feature description exploiting motion history images (MHI) is designed. By learning the proposed features using the support vector machine framework, various representative cattle actions, such as mounting, walking, tail wagging, and foot stamping, can be recognized robustly in complex scenes. Thanks to low complexity of the proposed action recognition algorithm, multiple cattle in three enclosures can be monitored simultaneously using a single fisheye camera. Through extensive experiments with real video streams, we confirmed that the proposed algorithm outperforms a conventional human action recognition algorithm by 18% in terms of recognition accuracy even with much smaller dimensional feature description.
Keywords
Action Recognition; Cow Behavior; Surveillance; Monitoring Cattle; SVM;
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Times Cited By KSCI : 1  (Citation Analysis)
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