Moving Object Segmentation Using Optical Flow and Depth Information
Jens Klappstein,Tobi Vaudrey,Clemens Rabe,Andreas Wedel,Reinhard Klette +4 more
- 09 Jan 2009
- Vol. 2009, pp 611-623
TL;DR: Object detection is based on motion analysis of individually tracked image points, providing a motion metric which corresponds to the likelihood that the tracked point is moving, and is segmented into objects by employing a globally optimal graph-cut algorithm.
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Abstract: This paper discusses the detection of moving objects (being a crucial part of driver assistance systems) using monocular or stereoscopic computer vision. In both cases, object detection is based on motion analysis of individually tracked image points (optical flow), providing a motion metric which corresponds to the likelihood that the tracked point is moving. Based on this metric, points are segmented into objects by employing a globally optimal graph-cut algorithm. Both approaches are comparatively evaluated using real-world vehicle image sequences.
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Citations
Visual SLAM and Structure from Motion in Dynamic Environments: A Survey
TL;DR: This article presents for the first time a survey of visual SLAM and SfM techniques that are targeted toward operation in dynamic environments and identifies three main problems: how to perform reconstruction, how to segment and track dynamic objects, and how to achieve joint motion segmentation and reconstruction.
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Visual SLAM for robot navigation in healthcare facility
TL;DR: Wang et al. as mentioned in this paper proposed a novel SLAM technology using RGB and depth images to improve hospital operation efficiency, reduce the risk of doctor-patient cross-infection, and curb the spread of the COVID-19 pandemic.
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Review of optical flow technique for moving object detection
Anshuman Agarwal,Shivam Gupta,Dushyant Kumar Singh +2 more
- 01 Dec 2016
TL;DR: This paper presents one of such method which is termed as Optical Flow technique, found to be more robust and efficient for moving object detection and the same has been shown by an experiment in the paper.
73
A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation
Kai Wang,Lin Yimin,Wang Luowei,Han Liming,Minjie Hua,Xiang Wang,Lian Shiguo,Bill Huang +7 more
- 20 May 2019
TL;DR: This paper presents a novel framework for simultaneously implementing localization and segmentation, which is able to handle both the instantaneous motion and long-term changes of instances in localization with the help of the segmentation result, which also benefits from the refined 3D pose information.
Dense 3D SLAM in Dynamic Scenes Using Kinect
Mohammed Chafik Bakkay,Majdi Arafa,Ezzeddine Zagrouba +2 more
- 17 Jun 2015
TL;DR: Quantitative evaluations show that the proposed method produces a real-time 3D reconstruction with higher accuracy and lower trajectory error compared to the state-of-the-art methods.
44
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