Detection, Segmentation, and Tracking of Moving Objects in UAV Videos

Automatic processing of videos coming from small UAVs offers high potential for advanced surveillance applications but is also very challenging. These challenges include camera motion, high object distance, varying object background, multiple objects near to each other, weak signal-to-noise-ratio (SNR), or compression artifacts. In this paper, a video processing chain for detection, segmentation, and tracking of multiple moving objects is presented dealing with the mentioned challenges. The fundament is the detection of local image features, which are not stationary. By clustering these features and subsequent object segmentation, regions are generated representing object hypotheses. Multi-object tracking is introduced using a Kalman filter and considering the camera motion. Split or merged object regions are handled by fusion of the regions and the local features. Finally, a quantitative evaluation of object segmentation and tracking is provided.