Dynamic Background Reconstruction in Traffic Surveillance Systems

Chen, Dongya; Zhang, Changmao; Wang, Shoujun; Tian, Tao

doi:10.1109/isccs.2011.74

Cited by 2 publications

(2 citation statements)

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“…Moving object detection in surveillance videos is an important aspect of computer vision [1]. The commonly used method to detect moving objects involves identification of foreground pixels in the video frame, followed by classification and determination of the objects from the foreground pixels [2][3][4][5]. However, the detecting algorithm can be easily disturbed by the cast shadow, which refers to the part of the background area where lacks of irradiation as the light block of the moving object.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive shadow detection using global texture and sampling deduction

Jiang

Cui

et al. 2013

IET Computer Vision

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An adaptive shadow detection algorithm is proposed to eliminate interference on object detection from the shadow. The algorithm uses three components in YUV colour space to identify shadow pixels from the candidate foreground. An adaptive threshold estimator is designed to improve shadow detection accuracy and adaptive capacity in various lighting conditions. This estimator uses edge detection method to obtain global texture, as well statistical calculations to obtain the thresholds. Algorithm has the characteristic of low complexity and little restraint; hence it is suitable for real time-moving shadow detection in various lighting conditions. Experiment results show that this algorithm can obtain a high detection accuracy and the time-assume is greatly shortened compared with other algorithms with similar accuracy.

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