Depth Estimation in Still Images and Videos Using a Motionless Monocular Camera

被引:0
|
作者
Diamantas, Sotirios [1 ]
Astaras, Stefanos [1 ]
Pnevmatikakis, Aristodemos [1 ]
机构
[1] Athens Informat Technol, Multimodal Signal Analyt, 44 Kifisias Ave, Athens 15125, Greece
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this research we address the problem of depth estimation using a single motionless monocular camera. In our method we make no use of reference objects or marks in the image plane or on the ground apart from a one-off object used for horizon line detection; even this, however, is not necessary if a vanishing point detection algorithm is employed. Camera height is the only known parameter that is projected onto the image plane. Our algorithm has been tested using both a light calibrated and a non-calibrated camera and the results presented demonstrate that it works exceptionally well with both options. Our method promises to relax several assumptions and restrictions followed by state-of-the-art methods such as the height or width of the object of interest. Furthermore, our algorithm has been tested on still images as well as on videos using a background subtraction algorithm for automatic segmentation of foreground moving objects. The results obtained demonstrate our method is accurate and useful to a variety of applications from robot navigation to target tracking.
引用
收藏
页码:129 / 134
页数:6
相关论文
共 50 条
  • [31] Real-Time Depth Estimation from a Monocular Moving Camera
    Handa, Aniket
    Sharma, Prateek
    CONTEMPORARY COMPUTING, 2012, 306 : 494 - 495
  • [32] A variational approach for estimation of monocular depth and camera motion in autonomous driving
    Hu, Huijuan
    Hu, Chuan
    Zhang, Xuetao
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, 2022, 236 (05) : 794 - 804
  • [33] Towards camera parameters invariant monocular depth estimation in autonomous driving
    Koledic, Karlo
    Markovic, Ivan
    Petrovic, Ivan
    2023 EUROPEAN CONFERENCE ON MOBILE ROBOTS, ECMR, 2023, : 215 - 221
  • [34] Region Deformer Networks for Unsupervised Depth Estimation from Unconstrained Monocular Videos
    Xu, Haofei
    Zheng, Jianmin
    Cai, Jianfei
    Zhang, Juyong
    PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2019, : 5685 - 5691
  • [35] Self-supervised monocular depth estimation from oblique UAV videos
    Madhuanand, Logambal
    Nex, Francesco
    Yang, Michael Ying
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2021, 176 : 1 - 14
  • [36] DEPTH ESTIMATION FROM MONOCULAR COLOR IMAGES USING NATURAL SCENE STATISTICS MODELS
    Su, Che-Chun
    Cormack, Lawrence K.
    Bovik, Alan C.
    2013 IEEE 11TH IVMSP WORKSHOP: 3D IMAGE/VIDEO TECHNOLOGIES AND APPLICATIONS (IVMSP 2013), 2013,
  • [37] Depth estimation of supervised monocular images based on semantic segmentation
    Wang, Qi
    Piao, Yan
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2023, 90
  • [38] Distortion-Aware Monocular Depth Estimation for Omnidirectional Images
    Chen, Hong-Xiang
    Li, Kunhong
    Fu, Zhiheng
    Li, Mengyi
    Chen, Zonghao
    Guo, Yulan
    IEEE SIGNAL PROCESSING LETTERS, 2021, 28 (28) : 334 - 338
  • [39] Depth Estimation from Monocular Images and Sparse Radar Data
    Lin, Juan-Ting
    Dai, Dengxin
    Van Gool, Luc
    2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2020, : 10233 - 10240
  • [40] Depth360: Self-supervised Learning for Monocular Depth Estimation using Learnable Camera Distortion Model
    Hirose, Noriaki
    Tahara, Kosuke
    2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2022, : 317 - 324