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Vehicle Segmentation and Speed Detection Based on Binocular Stereo Vision

Authors: Xin Li; Zenggang Zhou; Xiaoyuan Li; Yan Wan;

Vehicle Segmentation and Speed Detection Based on Binocular Stereo Vision

Abstract

Real-time motion detection is a key technology in the intelligent video surveillance and traffic video flow. Widely used methods based on monocular vision are sensitive to light and have problems of black hole and shadow. To solve these problems, we proposed a motion detection method based on binocular vision. The segmentation of move object is based on the distinctive of depth information which is obtained from two camera's parallax. Based on the results of segmentation and related depth information we proposed a novel speed detection algorithm which can adapt to a variety of space shooting angles. Experimental results show that our method obtained accurate contour and speed of the moving object which satisfactorily solved the problems brought by the monocular vision method and achieved the state-of-the-art performance.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
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