Abstract: At present, underground regional personnel restriction algorithms based on video monitoring mainly rely on multi-object tracking, which has problems such as complex computation that makes low-power embedded deployment difficult and susceptibility to environmental light interference. To address the above issues, a lightweight algorithm for coal mine regional personnel restriction based on object detection and tracking was proposed. Multiple binocular structured-light cameras were used to collect color and depth images at key entrances and exits. Based on the improved lightweight object detection model GF-YOLOv8, worker image localization was achieved. An Intersection-Over-Union priority strategy was adopted to match objects and trajectories, which reduced the number of matches compared with traditional tracking algorithms and improved the computational speed of tracking. Based on tracking trajectories, the total number of people in key areas was counted through the cross-line counting method with multiple cameras. When the number of people in the area reached the personnel limit, alarm information was generated. The experimental results showed that binocular structured-light cameras effectively overcame the influence of illumination. The AP@0.5 and AP@0.5:0.95 of the improved detection model GF-YOLOv8 increased by 0.24% and 3.42% respectively compared with YOLOv8, while the number of parameters and floating point operations per second decreased by 17.29% and 2.58% respectively. The Intersection-Over-Union priority strategy achieved accuracy close to the DeepSort algorithm but with lower matching complexity. Regarding the count of personnel restriction alarms, there were 17 true alarms; the Intersection-Over-Union priority strategy correctly reported 16 and missed 1 alarm.