# Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments
## 2 Related Works  
In this section, we review the recent achievements related to our proposed approach. For a comprehensive overview of existing tracking methods, readers can refer to the cited articles.  
The tracking-by-detection method regards the target tracking in each frame as a detection problem in a local search window. The method usually separates the target from its surrounding background by an incremental learning classifier [11; 19; 20; 21]. In this kind of study, each frame in the image sequence is regarded as a single target detection process. This approach is currently the most commonly used method for visual target tracking.  
In 2011, [PERSON] et al. proposed the MOSSE [10] filter for tracking, which exhibited an impressive speed of more than 600 frames per second (FPS), demonstrating the potential  
Figure 2: The flowchart of the proposed tracking method. The projection matrix \(\mathbf{P}\) was applied to the LCT [18] architecture to reduce the computational complexity. The need for image enhancement is evaluated before sample training. Image enhancement is performed iteratively until the image reaches the brightness threshold.  
of the correlation filter. DCF-based trackers use fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) for learning and rapid detection in the frequency domain. Thus, they have a low computing time by means of correlation operation on image features. Many DCF-based trackers have been proposed for feature extraction with a learning filter architecture [10; 11; 12; 13; 14; 20; 22]. However, most sacrifice the tracking speed to achieve accurate and robust tracking performance.  
In recent years, deep learning (DL) has been widely utilized in computer vision, language processing, and intelligent robotics. As a representative architecture, convolutional neural networks (CNNs) have achieved remarkable results in visual tracking due to their powerful feature expression ability. Three types of CNNs have been used for VOT: (a) CNNs based on pure convolutional features [23]; (b) Siamese network-based trackers [21; 24; 25]; (c) DCF-based trackers based on the VGG-Net [26] and other network training features [22; 27; 28]. These trackers provide high-precision results by utilizing the graphics card. However, they require many training samples and high computational power, limiting their application on mobile devices.  
Long-term tracking differs from short-term tracking because of long-term occlusion and field-of-view conditions. [PERSON] et al. [29] first proposed this tracking framework and decomposed the long-term tracking task into three parts: tracking, learning, and detection (TLD). The long-term correlation tracker (LCT) [18] and LCT+ [30] were proposed by [PERSON] et al. in the framework of the convolution kernel correlation filter tracker. A re-detector and a long-time filter were added to the general short-term tracker architecture. [PERSON] et al. proposed a novel collaborative correlation tracker (CCT) [31] using multi-scale kernelized correlation tracking (MKC) and an online CUR [32] filter for long-term tracking. [PERSON] et al. [33] proposed a Skimming-Perusal tracking framework, which is based on deep networks and SiameseRPN [34] to achieve real-time and robust long-term tracking works.  
Low illumination tracking task has been a key concern in coal mine application, [PERSON] et al. [35] prefer to use a Kinect camera for low illumination tracking task. [PERSON] et al. [36] proposed a dual correlation filtering structure for tracking in dark light, which is called an anti-dark tracker (ADTrack). [PERSON] et al. [37] tended to use a CNNs-based framework to realize reliable UAV tracking at night and proposed a spatial-channel transformer-based low-light enhancer (SCT) [38].