Deer Body Adaptive Threshold Segmentation Algorithm Based on Color Space.

Yuheng Sun; Ye Mu; Qin Feng; Tianli Hu; He Gong; Shijun Li; Jing Zhou

In large-scale deer farming image analysis, K-means or maximum between-class 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.

DEER; DIGITAL image processing; SIKA deer; POSTURE; IMAGE analysis

Computers, Materials & Continua, 2020, Vol 64, Issue 2, p1317

1546-2218

Academic Journal

10.32604/cmc.2020.010510