## 2 Materials and Methods
### Multi-Feature Fusion  
Selecting the object features is crucial, as it directly affects the object tracking results. This paper extracts the object's hand-crafted and deep features for adaptive feature fusion to enhance the object's description and improve the tracking algorithm performance in a complex background. Considering the hand-crafted features, this paper employs HOG features, mainly containing texture information with high spatial resolution and high target localization accuracy. However, solely relying on HOG features does not afford accurate target tracking in complex background scenarios. Considering depth features for complex backgrounds and illumination variation cases, CNN features have rich texture information and stronger robustness but present low spatial resolution and localization accuracy for the targets. Therefore, this paper fuses the hand-crafted and deep feature response to fully utilize their complementary nature and improve the filter's classification ability. Hence, we rely on the VGG network to extract the object's in-depth features. VGG is a model proposed by Oxford University in 2014 that has demonstrated appealing results in image classification and target detection tasks. Specifically, each image is input into VGG, and its convolutional layers provide the deep features. Deep convolutional neural networks can extract rich features in spatial and semantic information. The low-level convolutional features contain rich spatial information, and the high-level convolutional features contain rich semantic information. The layered convolutional correlation filter tracking algorithm [26] uses the features extracted from the conv3-5, conv4-5, and conv5-5 layers of VGG-19 to predict the target location. Precisely, the features in the correlation filtering framework weight the response maps of the three layers, achieving a significant improvement in tracking accuracy compared with the traditional feature correlation filtering algorithm. Therefore, the VGG-19 network is experimentally validated by selecting low, middle, and high-level convolutional layers that contain spatial and semantic information, i.e., conv2-3, conv3-4, and conv4-4, depicted in Figure 3. Although the features extracted from the fifth convolutional layer are semantically rich, these do not further improve the object's description (see Figure 3e), and thus we extract features from the convolutional layer. Nevertheless, the large dimensionality of the deep convolutional layer features  
Figure 2: Flowchart of the proposed algorithm. CF and AKF are the abbreviations of correlation filter and adaptive Kalman filter. The Euclidean distance is the object center position of adjacent frames. increases the computational complexity, especially when using multiple convolutional layer features during training the filters, resulting in slower tracking. Additionally, the compelling convolutional features differ depending on the tracking scenarios. Figure 4 illustrates the CNN layer-2 features of Figure 3a, where some features contribute less or are even invalid for the tracking task, reducing the tracking performance and slowing down the tracking speed. Hence, the appropriate convolutional channels need to be selected considering speed and robustness. Thus, the features are filtered by the ratio of the object and the object search region variances:  
\[V_{1}=\frac{1}{h_{1}\times w_{1}}\sum_{i,j}\Bigl{(}E_{i,j}-E\Bigr{)}^{2} \tag{1}\]  
\[V_{2}=\frac{1}{h_{2}\times w_{2}}\sum_{m,n}\Bigl{(}T_{m,n}-T\Bigr{)}^{2} \tag{2}\]  
so the variance ratio (\(VR\)) can be expressed as:  
\[VR=\frac{V_{1}}{V_{2}} \tag{3}\]  
where \(E_{i,j}\), \(T_{m,n}\) are the pixel values per layer of the object region and the object search region, respectively; and \(\overline{E}\), \(\overline{T}\) are the average pixel values of the object region and the object search region. \(i,j\) and \(m,n\) are the horizontal and vertical coordinates of the object region and the object search region, respectively.