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Moving objects detection using a thermal Camera and IMU on a vehicle

机译:使用热像仪和IMU在车辆上检测运动物体

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In this paper we present a novel algorithm for moving object detection in thermal images taken by a moving thermal camera. It allows a detection of moving objects in thermal images of low quality without imposing restrictions on the temperature and/or shape of the object. The main assumption required for good performance of the algorithm is that the transversal movement of the vehicle will not produce significant change in the optical flow of the static objects in the scene between two consecutive image frames. Our algorithm does not use any temperature thresholds and works well in urban environments detecting moving humans and other moving objects as well. To achieve this we use fusion of an inertial measurement unit (IMU) and a thermal camera. First we use IMU data to compensate for rotational movements of the thermal camera between two consecutive thermal images. Then we differentiate those images and filter the resulting image based on dense optical flow calculated using Farneback technique. After that moving objects are detected and further filtering is applied using random sampling consensus algorithm based on optical flow model.
机译:在本文中,我们提出了一种在移动热像仪拍摄的热图像中检测运动物体的新颖算法。它允许在低质量的热图像中检测运动物体,而不会对物体的温度和/或形状施加限制。算法良好性能所需的主要假设是,车辆的横向运动不会在两个连续图像帧之间的场景中产生静态物体的光流显着变化。我们的算法不使用任何温度阈值,并且在检测移动的人和其他移动物体的城市环境中也能很好地工作。为此,我们使用了惯性测量单元(IMU)和热像仪的融合。首先,我们使用IMU数据来补偿热像仪在两个连续的热图像之间的旋转运动。然后,我们区分这些图像并基于使用Farneback技术计算的密集光流对生成的图像进行滤波。之后,检测到运动物体,并使用基于光流模型的随机采样共识算法进行进一步滤波。

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