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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2016.1
- 수록면
- 277 - 285 (9page)
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Moving object detection system has been an emerging research field in various advanced driver assistance systems (ADAS) and surveillance system. In this paper, we propose two optical flow based moving object detection methods at dynamic scenes. Both proposed methods consist of three successive steps; pre-processing, foreground segmentation, and post-processing steps. Two proposed methods have the same pre-processing and post-processing steps, but different foreground segmentation step. Pre-processing calculates mainly optical flow map of which each pixel has the amplitude of motion vector. Dense optical flows are estimated by using Farneback technique, and the amplitude of the motion normalized into the range from 0 to 255 is assigned to each pixel of optical flow map. In the foreground segmentation step, moving object and background are classified by using the optical flow map. Here, we proposed two algorithms. One is Gaussian mixture model (GMM) based background subtraction, which is applied on optical map. Another is adaptive thresholding based foreground segmentation, which classifies each pixel into object and background by updating threshold value column by column. Through the simulations, we show that both optical flow based methods can achieve good enough object detection performances in dynamic scenes.
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등록된 정보가 없습니다.
참고문헌 (13)
참고문헌 신청
Z. Zivkovic / 2004 / Improved adaptive Gaussian mixture model for background subtraction / Proceedings of IEEE the 17th International Conference on Pattern Recognition 2:28~31
A. Schick / 2012 / Improving foreground segmentations with probabilistic super pixel markov random fields / Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops :27~31
A. Mittal / 2004 / Motion-based background subtraction using adaptive kernel density estimation / Proceedings of the 2004IEEE Computer Society Conference on Computer Vision and Pattern Recognition :II-302~II-309
R. Cucchiara / 2003 / Detecting moving objects, ghosts, and shadows in video streams / IEEE Transactions on Pattern Analysis and Machine Intelligence 25(10):1337~1342
N.J. McFarlane / 1995 / Segmentation and tracking of piglets in images / Machine vision and applications 8(3):187~193
A. Schick / 2012 / Improving foreground segmentations with probabilistic super pixel markov random fields / Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops :27~31
A. Mittal / 2004 / Motion-based background subtraction using adaptive kernel density estimation / Proceedings of the 2004IEEE Computer Society Conference on Computer Vision and Pattern Recognition :II-302~II-309
R. Cucchiara / 2003 / Detecting moving objects, ghosts, and shadows in video streams / IEEE Transactions on Pattern Analysis and Machine Intelligence 25(10):1337~1342
N.J. McFarlane / 1995 / Segmentation and tracking of piglets in images / Machine vision and applications 8(3):187~193
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