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  • Lee, Gyu-cheol  (4)
  • Yoo, Jisang  (4)
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  • 1
    Online Resource
    Online Resource
    Disparity Refinement near the Object Boundaries for Virtual-View Quality Enhancement
    The Korean Institute of Electrical Engineers ; 2015
    In:  Journal of Electrical Engineering and Technology Vol. 10, No. 5 ( 2015-09-01), p. 2189-2196
    Details
    In: Journal of Electrical Engineering and Technology, The Korean Institute of Electrical Engineers, Vol. 10, No. 5 ( 2015-09-01), p. 2189-2196
    Type of Medium: Online Resource
    ISSN: 1975-0102
    URL: Article
    DOI: 10.5370/JEET.2015.10.5.2189
    Language: English
    Publisher: The Korean Institute of Electrical Engineers
    Publication Date: 2015
    detail.hit.zdb_id: 2255142-6
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Moving Object Detection Using an Object Motion Reflection Model of Motion Vectors
    MDPI AG ; 2019
    In:  Symmetry Vol. 11, No. 1 ( 2019-01-02), p. 34-
    Details
    In: Symmetry, MDPI AG, Vol. 11, No. 1 ( 2019-01-02), p. 34-
    Abstract: Moving object detection task can be solved by the background subtraction algorithm if the camera is fixed. However, because the background moves, detecting moving objects in a moving car is a difficult problem. There were attempts to detect moving objects using LiDAR or stereo cameras, but when the car moved, the detection rate decreased. We propose a moving object detection algorithm using an object motion reflection model of motion vectors. The proposed method first obtains the disparity map by searching the corresponding region between stereo images. Then, we estimate road by applying v-disparity method to the disparity map. The optical flow is used to acquire the motion vectors of symmetric pixels between adjacent frames where the road has been removed. We designed a probability model of how much the local motion is reflected in the motion vector to determine if the object is moving. We have experimented with the proposed method on two datasets, and confirmed that the proposed method detects moving objects with higher accuracy than other methods.
    Type of Medium: Online Resource
    ISSN: 2073-8994
    URL: Article
    DOI: 10.3390/sym11010034
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2518382-5
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  • 3
    Online Resource
    Online Resource
    3-D People Counting with a Stereo Camera on GPU Embedded Board
    MDPI AG ; 2018
    In:  Applied Sciences Vol. 8, No. 11 ( 2018-10-23), p. 2017-
    Details
    In: Applied Sciences, MDPI AG, Vol. 8, No. 11 ( 2018-10-23), p. 2017-
    Abstract: People counting in surveillance cameras is a key technology for understanding the flow population and generating heat maps. In recent years, people detection performance has been greatly improved with the development of object detection algorithms using deep learning. However, in places where people are crowded, the detection rate is low as people are often occluded by other people. We proposed a people-counting method using a stereo camera to resolve the non-detection problem due to the occlusion. We applied stereo matching to extract the depth image and convert the camera view to top view using depth information. People were detected using a height map and an occupancy map, and people were tracked and counted using a Kalman filter-based tracker. We operated the proposed method on the NVIDIA Jetson TX2 to check the real-time operation possibility on the embedded board. Experimental results showed that the proposed method had higher accuracy than the existing methods and that real-time processing is possible.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app8112017
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2704225-X
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  • 4
    Online Resource
    Online Resource
    WisenetMD: Motion Detection Using Dynamic Background Region Analysis
    MDPI AG ; 2019
    In:  Symmetry Vol. 11, No. 5 ( 2019-05-03), p. 621-
    Details
    In: Symmetry, MDPI AG, Vol. 11, No. 5 ( 2019-05-03), p. 621-
    Abstract: In this paper, we propose a method for calculating the dynamic background region in a video and removing false positives in order to overcome the problems of false positives that occur due to the dynamic background and frame drop at slow speeds. Therefore, we need an efficient algorithm with a robust performance value including processing speed. The foreground is separated from the background by comparing the similarities between false positives and the foreground. In order to improve the processing speed, the median filter was optimized for the binary image. The proposed method was based on a CDnet 2012/2014 dataset and we achieved precision of 76.68%, FPR of 0.90%, FNR of 18.02%, and an F-measure of 75.35%. The average ranking across categories is 14.36, which is superior to the background subtraction method. The proposed method was operated at 45 fps (CPU), 150 fps (GPU) at 320 × 240 resolution. Therefore, we expect that the proposed method can be applied to current commercialized CCTV without any hardware upgrades.
    Type of Medium: Online Resource
    ISSN: 2073-8994
    URL: Article
    DOI: 10.3390/sym11050621
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2518382-5
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