# Tracking System for a Coal Mine Drilling Robot for Low-Illumination Environments
## 1 Introduction  
Visual object tracking (VOT) is a fundamental problem and a popular research area in computer vision. Numerous studies have been conducted on this topic, producing multiple performance evaluation datasets and benchmarks [1; 2; 3; 4; 5]. The goal of the tracker is to select a model-free target from the first frame and track it in subsequent frames of a video stream or image sequence. Recently, VOT has been used for real-time vision applications, such as intelligent monitoring systems, automatic driving systems, and robotics [6; 7; 8]. Object tracking is an online task that needs to meet the requirements of practical applications. An ideal tracker should be accurate and robust for a long period in real-time vision systems. Moreover, due to the complexity of the working environment, including image deformation, object occlusion, illumination changes, motion blur, and objects out of view, the tracker is prone to drifting during long-term tracking, reducing its performance [9]. In recent years, DCF-based methods, including Minimum Output Sum of Squared Error (MOSSE) [10], kernelized correlation filter (KCF) [11], background-aware correlation filter (BACF) [12], discriminative correlation filter with channel and spatial reliability (CSR-DCF) [13], and efficient convolution operators (ECO) [14], have significantly advanced the state-of-the-art (SOTA) performance for short-term tracking. These methods have high processing speeds and are convenient for feature extraction, resulting in a new research direction in this field. Intelligent mobile robots have become a research hotspot in science and technology. Path planning, positioning and navigation, obstacle avoidance, and other aspects of mobile robots are inseparable from the assistance of vision technology [15]. Since mobile robots are limited by their endurance, volume, and flexibility, their hardware configuration is usually based on low power consumption. Thus, an algorithm with high computational complexity can substantially reduce the real-time performance of the robot. In practical engineering applications, intermittent light sources or uneven illumination will cause overexposure of the camera, which can adversely affect the performance of VOT. This problem is particularly pronounced in coal mines. The distribution form of a miner's lamp cannot make the light fill the whole tunnel uniformly (As shown in Figure 1). Most coal mines use infrared cameras to deal with low illumination, but these images are single-channel images (grayscale images). Infrared cameras are typically used to monitor fixed equipment, resulting in limitations in dynamic object tracking, such as personnel monitoring and mining vehicle scheduling. Relevant research shows that the performance of the vision algorithm is directly related to the richness of image features [16]. The performance of current trackers is affected by the number of characteristic channels. It is necessary to analyze the performance of target trackers using a color camera in low-illumination environments. Therefore, an algorithm to track dynamic targets in low-illumination environments with a color image is required. Moreover, this algorithm can also need to be applied to mobile robots with power consumption constraints. Hence, this work focuses on practical problems in engineering applications rather than dataset evaluations. We propose the low-illumination long-term correlation tracker (LLCT). It is adapted to a low-illumination environment, outperforms SOTA trackers of the time, and provides real-time and high-precision performance in the field environment. The LLCT uses an image exposure compensation method for low-illumination environments, making it suitable for coal mine mobile robots. The flowchart of the proposed method is illustrated in Figure 2.  
The long-term real-time correlation filter (LRCF) for mobile robots proposed in [17] has been significantly improved and extended in this paper. The contributions of our previous study are summarized as follows:  
1.