Open Access
ARTICLE
Deer Body Adaptive Threshold Segmentation Algorithm Based on Color Space
Yuheng Sun1, Ye Mu1, 2, 3, 4, *, Qin Feng5, Tianli Hu1, 2, 3, 4, He Gong1, 2, 3, 4, Shijun Li1, 2, 3, 4, Jing Zhou6
1 College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.
2 Jilin Province Agricultural Internet of Things Technology Collaborative Innovation Center, Changchun,
130118, China.
3 Jilin Province Intelligent Environmental Engineering Research Center, Changchun, 130118, China.
4 Jilin Province Colleges and Universities and the 13th Five-Year Engineering Research Center, Changchun,
130118, China.
5 Wuhan Maritime Communication Research Institute, Wuhan, 430205, China.
6 Fruit Research and Extension Center, Department of Agricultural and Biological Engineering, Penn State
University, PA, USA.
* Corresponding Author: Ye Mu. Email: .
Computers, Materials & Continua 2020, 64(2), 1317-1328. https://doi.org/10.32604/cmc.2020.010510
Received 07 March 2020; Accepted 21 April 2020; Issue published 10 June 2020
Abstract
In large-scale deer farming image analysis, K-means or maximum betweenclass variance (Otsu) algorithms can be used to distinguish the deer from the background.
However, in an actual breeding environment, the barbed wire or chain-link fencing has a
certain isolating effect on the deer which greatly interferes with the identification of the
individual deer. Also, when the target and background grey values are similar, the
multiple background targets cannot be completely separated. To better identify the
posture and behaviour of deer in a deer shed, we used digital image processing to
separate the deer from the background. To address the problems mentioned above, this
paper proposes an adaptive threshold segmentation algorithm based on color space. First,
the original image is pre-processed and optimized. On this basis, the data are enhanced
and contrasted. Next, color space is used to extract the several backgrounds through
various color channels, then the adaptive space segmentation of the extracted part of the
color space is performed. Based on the segmentation effect of the traditional Otsu
algorithm, we designed a comparative experiment that divided the four postures of
turning, getting up, lying, and standing, and successfully separated multiple target deer
from the background. Experimental results show that compared with K-means, Otsu and
hue saturation value (HSV)+K-means, this method is better in performance and accuracy
for adaptive segmentation of deer in artificial breeding scenes and can be used to separate
artificially cultivated deer from their backgrounds. Both the subjective and objective
aspects achieved good segmentation results. This article lays a foundation for the
effective identification of abnormal behaviour in sika deer.
Keywords
Cite This Article
Y. Sun, Y. Mu, Q. Feng, T. Hu, H. Gong
et al., "Deer body adaptive threshold segmentation algorithm based on color space,"
Computers, Materials & Continua, vol. 64, no.2, pp. 1317–1328, 2020. https://doi.org/10.32604/cmc.2020.010510