@Article{cmc.2020.010510,
AUTHOR = {Yuheng Sun, Ye Mu, Qin Feng, Tianli Hu, He Gong, Shijun Li, Jing Zhou},
TITLE = {Deer Body Adaptive Threshold Segmentation Algorithm Based on Color Space},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {1317--1328},
URL = {http://www.techscience.com/cmc/v64n2/39362},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.010510}
}