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Touching-Pigs Segmentation using Concave Points in Continuous Video Frames

Published: 10 August 2017 Publication History

Abstract

Caring individual pigs in large-scale pig farms is an important issue for preventing infectious diseases. Accordingly, many researchers have been researched about group-housed pig monitoring systems. However, it is challenging to identify individual pigs because the systems misidentify touching-pigs as a single pig. In this paper, we solve the touching-pig problem by using concave points of continuous video frames. We interpret a two dimensional outline data as a one dimensional time-series data of touching pigs and align the time-series data of continuous video frames. The experimental results show that the proposed method can segment the touching-pigs more accurate than generally used methods in real time.

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Cited By

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  • (2019)Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic reviewPLOS ONE10.1371/journal.pone.022666914:12(e0226669)Online publication date: 23-Dec-2019

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  1. Touching-Pigs Segmentation using Concave Points in Continuous Video Frames

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    ICACS '17: Proceedings of the 1st International Conference on Algorithms, Computing and Systems
    August 2017
    117 pages
    ISBN:9781450352840
    DOI:10.1145/3127942
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 10 August 2017

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    Author Tags

    1. Image Segmentation
    2. Pigsty Monitoring Environment

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    • (2019)Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic reviewPLOS ONE10.1371/journal.pone.022666914:12(e0226669)Online publication date: 23-Dec-2019

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