Abstract: Underground coal mine is a typical low-illumination environment. In order to solve the problem of tracking failure due to insufficient feature extraction by general cameras in this environment, a low-illumination single-target tracker was proposed, which incorporated image enhancement algorithm and multi-channel background-aware correlation filter. The Augmented Lagrangian method was used to solve the optimization problem of image enhancement and target tracking. A method for determining the mean value of light in the target area was proposed to solve the problem of the interference of local ambient light source on the target brightness discrimination. The luminance detection criterion was set to realize the brightness discrimination and enhancement of a single frame image, which ensured the integrity of the target feature extraction when the illumination changes. Simulation experiments verified that the tracking accuracy and overlap of the present tracker were much higher than the general mainstream trackers under dark light conditions. The field test results show that the real-time frame rate of this tracker can reach 40 Hz and the average tracking overlap accuracy is 81.6%, which effectively solves the target tracking problem of robots using ordinary cameras in the low-illumination environment of coal mines.