Journal Article10.1109/JSEN.2017.2723599
Fast Motion Object Detection Algorithm Using Complementary Depth Image on an RGB-D Camera
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TL;DR: A new fast motion object-detection algorithm is presented based on the complementary depth images and color information, which is able to detect motion objects without background noise.
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Abstract: Stereo vision has become a popular topic in recent years, especially in-depth images from stereo vision. Depth information can be extracted either from a dual camera or RGB-D camera. In image processing, the realization of object detection is only based on the color information or depth images separately; however, both have advantages and disadvantages. Therefore, many researchers have combined them together to achieve better results. A new fast motion object-detection algorithm is presented based on the complementary depth images and color information, which is able to detect motion objects without background noise. The experiment results show that the proposed fast object detection algorithm can achieve 84.4% of the segmentation accuracy rate on average with a 45 FPS computation speed on an embedded platform.
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Citations
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Enhancing RGB-D SLAM Performances Considering Sensor Specifications for Indoor Localization
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Close-Proximity Detection for Hand Approaching Using Backscatter Communication
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A Velocity Estimation Technique for a Monocular Camera Using mmWave FMCW Radars
TL;DR: A complete implementation of camera–mmW radar late feature fusion to improve the camera’s velocity estimation performance is presented, implementing a lightweight ML model that successfully maps the mmW radar features to the camera, allowing it to perceive and estimate the dynamics of a target object without any calibration.
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