The paper focuses on the problem of vision-based obstacle detection and tracking for unmanned aerial vehicle navigation. A real-time object localization and tracking strategy from monocular image sequences is developed by effectively integrating the object detection and tracking into a dynamic Kalman model. At the detection stage, the object of interest is automatically detected and localized from a saliency map computed via the image background connectivity cue at each frame; at the tracking stage, a Kalman filter is employed to provide a coarse prediction of the object state, which is further refined via a local detector incorporating the saliency map and the temporal information between two consecutive frames. Compared to existing methods, the proposed approach does not require any manual initialization for tracking, runs much faster than the state-of-the-art trackers of its kind, and achieves competitive tracking performance on a large number of image sequences. Extensive experiments demonstrate the effectiveness and superior performance of the proposed approach.

文章研究了无人机导航中基于视觉的障碍物检测和跟踪问题，提出了一种实时的对象定位和跟踪策略，将对象检测和跟踪有效地集成到动态卡尔曼模型中，并使用卡尔曼滤波提供对象状态的粗略预测，而无需对其进行手动初始化，具有比同类技术更快的运行速度和竞争性的跟踪性能。

基于视觉的无人机感知系统实时航空目标定位与跟踪