Moving objects detecting and tracking for unmanned aerial vehicle

Moving objects detecting and tracking is important for future Unmanned Aerial Vehicles (UAVs). We propose a new approach to detect and track moving objects from the flying UAV. First, estimate the global-motion of the background by tracking features selected by KLT algorithm from frame to frame. In order to avoid features located on the foreground objects participating in motion estimation, feature effectiveness evaluation is employed. Then compensate the background with the transform model computed by RANSAC. Define the undefined area before applying frame difference method to the compensated frame and the current frame. Then initialize the tracking module with information obtained from the detecting module, which overcomes shortcomings of artificial orientation of traditional tracking algorithms. For tracking fast and robustly from UAVs, we design a new tracking algorithm by fusing Kalman prediction and Mean Shift Search together. The experimental results presented effectiveness of the whole detecting and tracking approach.