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JRM Vol.32 No.6 pp. 1193-1199
doi: 10.20965/jrm.2020.p1193
(2020)

Paper:

Outdoor Human Detection with Stereo Omnidirectional Cameras

Shunya Tanaka and Yuki Inoue

Osaka Institute of Technology
1-45 Chayamachi, Kita-ku, Osaka 530-8568, Japan

Received:
May 20, 2020
Accepted:
November 10, 2020
Published:
December 20, 2020
Keywords:
mobile robot, robot vision, outdoor human detection, localization
Abstract
Outdoor Human Detection with Stereo Omnidirectional Cameras

Depth estimation of target person by object recognition and color similarity

An omnidirectional camera can simultaneously capture all-round (360°) environmental information as well as the azimuth angle of a target object or person. By configuring a stereo camera set with two omnidirectional cameras, we can easily determine the azimuth angle of a target object or person per camera on the image information captured by the left and right cameras. A target person in an image can be localized by using a region-based convolutional neural network and the distance measured by the parallax in the combined azimuth angles.

Cite this article as:
Shunya Tanaka and Yuki Inoue, “Outdoor Human Detection with Stereo Omnidirectional Cameras,” J. Robot. Mechatron., Vol.32, No.6, pp. 1193-1199, 2020.
Data files:
References
  1. [1] S. Thrun, W. Burgard, and D. Fox, “Probabilistic robotics,” MIT Press, Cambridge, 2005.
  2. [2] A. F. Elaraby, A. Hamdy, and M. Rehan, “A Kinect-Based 3D Object Detection and Recognition System with Enhanced Depth Estimation Algorithm,” 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conf. (IEMCON 2018), pp. 247-252, 2019.
  3. [3] M. A. Haseeb, J. Guan, D. Ristic-Durrant, and A. Gräser, “Disnet: A novel method for distance estimation from monocular camera,” 10th Planning, Perception and Navigation for Intelligent Vehicles (PPNIV18), IROS, 2018.
  4. [4] T. Akita, Y. Yamauchi, and H. Fujiyoshi, “Stereo vision by combination of machine-learning techniques for pedestrian detection at intersections utilizing surround-view cameras,” J. Robot. Mechatron., Vol.32, No.3, pp. 494-502, 2020.
  5. [5] T. Trzcinski and V. Lepetit, “Efficient Discriminative Projections for Compact Binary Descriptors,” 12th European Conf. on Computer Vision (ECCV 2012), pp. 228-242, 2012.
  6. [6] T. Aoki, S. Mengcheng, and H. Watanabe, “Position Estimation and Distance Measurement from Omnidirectional Cameras,” The 80th Information Processing Society of Japan, Vol.2018, No.1, pp. 265-266, 2018.
  7. [7] A. Ohashi, Y. Tanaka, G. Masuyama, K. Umeda, D. Fukuda, T. Ogata, T. Narita, S. Kaneko, Y. Uchida, and K. Irie, “Fisheye stereo camera using equirectangular images,” 2016 11th France-Japan 9th Europe-Asia Congress on Mechatronics (MECATRONICS) /17th Int. Conf. on Research and Education in Mechatronics (REM), pp. 284-289, 2016.
  8. [8] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 779-788, 2016.
  9. [9] A. Farhadi and and J. Redmon, “Yolov3: An incremental improvement,” Computer Vision and Pattern Recognition, 2018.
  10. [10] J. Redmon and A. Farhadi, “YOLO9000: better, faster, stronger,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pp. 7263-7271, 2017.

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Last updated on Aug. 03, 2021