Mr. Peijun Shi Profile

Peijun Shi

at Univ of Iowa Hospitals and Clinics

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Publications (1)

Proceedings Article | 28 May 2004 Paper

Median model for background subtraction in intelligent transportation system

Peijun Shi, Elizabeth Jones, Qiuming Zhu

Proceedings Volume 5298, (2004) https://doi.org/10.1117/12.525553

KEYWORDS: Intelligence systems, Data modeling, Statistical modeling, Video, Image processing, Real time image processing, Cameras, Performance modeling, Roads, Statistical analysis

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This research is generally divided into two phases: the first phase deals with background image generation and vehicle detection, the second phase deals with vehicle tracking and video handoff. In the first phase we view the image as a mixture of three data distributions: vehicle, background and shadow. Thus the problem is modeled as a mixture of Gaussian problem and our goal is to separate the background data from other data distributions. We proposed a median model and an improved median model to separate the background data from mixture data and to generate background reference images. In median model we keep track of deviation between the median and its neighbors in a reordered pixel sequence. When sample size is big enough, the reordered pixel sequence is in what we called balanced-median model. This model is indicated by a very small deviation value. In this case the median of the pixel sequence falls in background set and could be used for background estimation. When sample size is not big enough, the reordered pixel sequence is in what we called shifted-median model. This model is indicated by a much bigger deviation value. In this case the median falls out of background set and are excluded for background estimation. This median model has an impressive performance to handle slow moving or even stationary vehicles. But the time complexity is still expensive for real time image processing. The improved median model is proposed to reduce the time complexity to a reasonable level. In improved median model, we take samples in a bigger time interval to make it capable of dealing with slow moving and stationary vehicles. The sample size from experimentation is obtained as a small constant value between 5 and 20. This small sample constant size could dramatically reduce the time complexity. As a complementary to this improved median model, a mask-classified updating method is introduced to update the background image in a short term and only classified background pixels are being used for updating. Threshold, erosion, dilation and connected components labeling are used for noise removing and object labeling. After the first phase, the vehicle information is separated from image and input to the second phase for video hand-off and vehicle tracking. In the second phase, the weighted intensity information and shape information for each vehicle is scored and minimum-distance classification method is used for vehicle match. More than 400 vehicles are tested. An overall detection rate of 100% and tracking rate of 74% are obtained in this system.

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