## 4 Experimental Methods
### Color Space Information Extraction  
A traditional red-green-blue (RGB) color space may not extract the desired color parameters, as RGB color space components always have strong correlations in wildlife monitoring images. Therefore, constructing the color space model to extract color parameters is a significant procedure in our proposed method. However, the weakened quality of the acquired wildlife monitoring images aggravates the difficulty of completing the color space model construction due to the different illumination variations in wild environments.  
In our algorithm, the CIELUV (LUV) color space is applied in the output of luminance and chrominance components step, as it has two distinguished advantages over other color spaces. One is that it has non-correlation between color components, and the other is that it has been validated to extract detailed edge regions in the color image [28].  
To obtain more detailed color parameters, novel color enhancement based on the Retinex method was introduced into the color space model. The classic Retinex model decomposes images into reflectance and illumination:  
\[log(I(x,y))=log(R(x,y))+log(L(x,y)) \tag{1}\]  
where \(I(x,y)\) is the observed pixel in the monitoring image at the location of \((x,y)\)[29, 30], \(R(x,y)\) is the reflectance, and \(L(x,y)\) denotes the illumination of the image.  
In the theory of multi-scale Retinex, a plurality of individual convolutions with different Gaussian kernels can be applied to the original \(I(x,y)\) to approximate the component of \(L(x,y)\) by using different weights, as shown in Equation (2), where \(\sigma_{i}\) is the Gaussian Kernel modulus, and the sum of the weights \(w_{i}\) is equal to 1.  
\[\begin{array}{l}R_{\text{MSR}}(x,y)=\sum\limits_{i=1}^{n}w_{i}[log(I(x,y))- log(g(\sigma_{i})\times I(x,y))]\\ g(\sigma_{i})=\frac{1}{2\pi\sigma_{i}^{2}}e^{-\frac{((x-y)^{2}+(y-y)^{2})}{2 \pi\sigma_{i}^{2}}}\end{array} \tag{2}\]  
The novel color enhancement was inspired by the traditional Retinex framework, and it processes the wildlife monitoring images in the LUV color space. The color shift occurs when the traditional Retinex with a Gaussian Filter simultaneously changes the luminance and chrominance. Therefore,  
Figure 3: Flow-process diagram of the proposed method; LUV:CIELUV.  
the proposed method utilizes a bilateral filter in the luminance channel to avoid color shift, while the traditional Retinex algorithm utilizes Gaussian filtering in different color space channels. The bilateral filters with different kernels are used to process the L channel by the same weights considering the uneven distribution in the spatial domain, as shown in Equation (3), where \(\sigma_{i}\) is the kernel coefficient.  
\[R_{Bilateral-MSR}(x,y)=\sum_{i=1}^{n}w_{i}[log(I(x,y))-log(g_{Bilateral}( \sigma_{i})\times I(x,y))] \tag{3}\]  
The workflow of the proposed method for reconstructing the color space model is denoted in Figure 4, which can be divided into three main parts: (a) color space transformation, (b) the color enhancement with the filter in the middle, and (c) the extraction of the color parameters. The whole process of the method consists of the following steps:  
1. Convert the wildlife monitoring images from the RGB color space to the LUV color space;
2. Apply convolutions with a bilateral filter to the L channel of the motoring image to obtain the luminance component \(\hat{L}\). The \(\sigma_{i}\) values of the bilateral filter represent different scales, which are 10, 50, and 100 for three scales;
3. Preserve the original chrominance parameters with the \(U\)and \(V\)channel;
4. Reconstruct the color space model with \(\hat{L}UV\).