## 1 Introduction  
As an important part of ecological environment protection, wildlife protection is crucial for maintaining the balance and stability of the whole ecosystem [1]. Presently, the widely used wildlife monitoring methods are mainly based on GPS collar systems [2; 3], infrared camera technology [4; 5], and remote sensing monitoring technology [6; 7]. Although GPS collar systems can obtain wildlife locations with a great accuracy and precision, this monitoring method cannot obtain wildlife images, which poses a problem for researchers. The monitoring image, which can accurately estimate species diversity, population size, and habitat distribution, is an important part of wildlife protection. Thus, the monitoring image can provide a scientific basis for wildlife resource conservation [8]. Infrared camera technology and remote sensing monitoring technology can capture wildlife images, but these methods also have limitations. Infrared camera images need to be saved on secure digital (SD) memory cards, thus resulting in long monitoring delays and high labor costs. Remote sensing systems are capable of obtaining species diversity over a large geographic area, but cannot monitor wildlife at an individual level. In order to avoid these problems, the use of wireless multimedia sensor networks (WMSNs), which are used as monitoring carriers, has received extensive attention from researchers [9].  
Wireless sensor nodes usually rely on batteries for their power supply, but this setup is difficult to recharge in remote areas. Reducing the consumed energy and extending the life cycle of sensor networks has recently become a hot topic of research [10]. Meanwhile, the energy consumption of the sensor node is mainly concentrated in the data transmission process. Therefore, the progressive transmission of monitoring images in WMSNs can effectively reduce the energy consumption and improve the life cycle of nodes. At the same time, extraction of the image is the basis of progressive transmission [11; 12]. The current research work cannot achieve the extraction of wildlife monitoring images with a complex background and uneven illumination. Against this background, this study proposes an image extraction algorithm with a high accuracy for wildlife monitoring images. In this paper, we propose an extraction method for wildlife monitoring images based on the combination of color space construction and Hermite transform for WMSNs. Firstly, we reconstruct a color space model for utilizing the novel color enhancement method to extract the color parameter. The color enhancement method utilizes a bilateral filter with different kernels to process the luminance components based on the Retinex framework. Then, we construct a filter through Hermite transform to acquire texture information in the wildlife monitoring images. Finally, according to the characteristics of the pixel, an adaptive mean-shift algorithm is presented as an ideal clustering model to implement the extraction of the foreground area in the image. In this work, we took the wildlife of the Saihan Ula Nature Reserve in Inner Mongolia province as targets, and applied the proposed method to extract the wildlife images captured by the WMSN monitoring system. The experimental results showed that the proposed method achieved effective extraction results for the wildlife monitoring images and provided effective support for reducing the power consumption when transmitting wildlife monitoring images.