A Video-Based Real-Time Tracking Method for Multiple UAVs in Foggy Weather

Aiming at the real-time tracking problem of multiple unmanned aerial vehicles (UAVs) based on video under fog conditions, we propose a multitarget real-time tracking method that combines the Deepsort algorithm with detection based on improved dark channel defogging and improved You Only Look Once version 5 (YOLOv5) algorithm. The contributions of this paper are as follows: 1. For the multitarget tracking problem under fog interference, a multialgorithm combination method is proposed. 2. By optimizing dark channel defogging, the complexity of the original algorithm is reduced from O(n(2)) to O (n), which simplifies the processing time of the defogging algorithm. 3. The YOLOv5 network structure is optimized so that the network can synchronously reduce the detection time while maintaining high-precision detection. 4. The amount of algorithm processing through image size compression is reduced, and the real-time performance under high-precision tracking is improved. In the experiments conducted, the proposed method improved tracking precision by 36.1% and tracking speed by 39%. The average time of tracking per image frame was 0.036s, satisfying the real-time tracking of multiple UAVs in foggy weather.