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Proceedings Paper

The research of moving object detection with a moving camera
Author(s): Hai-xin Chen; Guo-hua Gu; Xiao-feng Bai; Tie-kun Zhao; Fu-yuan Xu
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Paper Abstract

With the rapid development of computer technology and mobile devices, high requirements for computer vision are more and more critical. In particular, current research on moving target detection based on still scene or hardware platform with turntable and gyroscope, can not meet the requirements of portable mobile equipment. Moving target detection and tracking on mobile platforms is attracting more and more attention. What makes the task even more challenging is when the camera is non-stationary ,due to the random motion of camera caused by bumps and swings of vehicle and handheld, and parallax problem caused by 3D scene. The essential problem in this case lies in distinguishing between global motion induced by camera and independent motion caused by moving targets. To solve above problems, the three-dimensional reconstruction method based on camera calibration technology is always introduced, such as the fundamental matrix and trifocal tensor, but that are appropriate for large classes of problems and situations without considering the complexity and speed of processing. This paper proposes a new robust algorithm GMOS (Global Motion Of Scene) based on the global motion of scene for moving object detection on a freely moving camera. By modeling for GMOS with the adjacent optical flow field of the image sequence, the proposed method is able to detect and separate the moving targets simply and fast from the global motion model without three-dimensional reconstruction. According to the GMOS model, we can describe the movement of the camera through a GMOS vector which is independent of two viewpoints between the adjacent images, and compensate for the overall movement caused by camera movement. The results of theory analysis and experimentations on numerous real world videos demonstrate that the proposed method GMOS could separate the independent objects fast and robustly under the premise of the high accuracy and robustness.

Paper Details

Date Published: 11 September 2013
PDF: 8 pages
Proc. SPIE 8907, International Symposium on Photoelectronic Detection and Imaging 2013: Infrared Imaging and Applications, 89071Z (11 September 2013); doi: 10.1117/12.2032885
Show Author Affiliations
Hai-xin Chen, Nanjing Univ. of Science and Technology (China)
Guo-hua Gu, Nanjing Univ. of Science and Technology (China)
Xiao-feng Bai, Science and Technology on Low-Light-Level Night Vision Lab. (China)
Tie-kun Zhao, Xi'an Sicong Chuangwei Opto-Electronic Co., Ltd. (China)
Fu-yuan Xu, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 8907:
International Symposium on Photoelectronic Detection and Imaging 2013: Infrared Imaging and Applications
Haimei Gong; Zelin Shi; Qian Chen; Jin Lu, Editor(s)

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