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A Real-Time Method to Detect and Track Moving Objects (DATMO) from Unmanned Aerial Vehicles (UAVs) Using a Single Camera

机译:一种使用单个摄像机检测和跟踪无人机(UAV)的运动对象(DATMO)的实时方法

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We develop a real-time method to detect and track moving objects (DATMO) from unmanned aerial vehicles (UAVs) using a single camera. To address the challenging characteristics of these vehicles, such as continuous unrestricted pose variation and low-frequency vibrations, new approaches must be developed. The main concept proposed in this work is to create an artificial optical flow field by estimating the camera motion between two subsequent video frames. The core of the methodology consists of comparing this artificial flow with the real optical flow directly calculated from the video feed. The motion of the UAV between frames is estimated with available parallel tracking and mapping techniques that identify good static features in the images and follow them between frames. By comparing the two optical flows, a list of dynamic pixels is obtained and then grouped into dynamic objects. Tracking these dynamic objects through time and space provides a filtering procedure to eliminate spurious events and misdetections. The algorithms have been tested with a quadrotor platform using a commercial camera.
机译:我们开发了一种实时方法,可以使用单个摄像机检测和跟踪无人驾驶飞机(UAV)的移动物体(DATMO)。为了解决这些车辆的挑战性特征,例如连续的不受限制的姿势变化和低频振动,必须开发新的方法。这项工作中提出的主要概念是通过估计两个后续视频帧之间的摄像机运动来创建人造光流场。该方法的核心在于将这种人工流与直接从视频源计算出的实际光流进行比较。利用可用的并行跟踪和映射技术可以估计无人机在帧之间的运动,这些技术可以识别图像中的良好静态特征并在帧之间跟随它们。通过比较两个光流,可以获得动态像素列表,然后将其分组为动态对象。在时间和空间上跟踪这些动态对象可提供一种过滤程序,以消除虚假事件和误检测。该算法已使用商用相机在四旋翼平台上进行了测试。

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