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

机译:使用车辆上的热相机和IMU移动物体检测

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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:本文介绍了一种用于移动热相机拍摄的热图像中的物体检测的新算法。它允许检测低质量的热图像中的移动物体,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移动不会在两个连续图像帧之间的场景中的静态物体的光流中产生显着变化。我们的算法不使用任何温度阈值,并且在城市环境中均良好地检测移动人类和其他移动物体。为了实现这一点,我们使用惯性测量单元(IMU)和热相机的熔合。首先,我们使用IMU数据来补偿两个连续的热图像之间热相机的旋转运动。然后,我们将这些图像区分开来,并基于使用FARNEBACK技术计算的致密光学流过滤所得到的图像。之后,使用基于光学流模型的随机采样共识算法施加进一步的滤波之后。

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