Moving shadow and object detection in traffic scenes

We present an algorithm for segmentation of traffic scenes that distinguishes moving objects from their moving cast shadows. A fading memory estimator calculates mean and variance of all three color components for each background pixel. Given the statistics for a background pixel, simple rules for c...

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Bibliographic Details
Published in:Proceedings 15th International Conference on Pattern Recognition. ICPR-2000 2000, Vol.1, p.321-324 vol.1
Main Authors: Mikic, I., Cosman, P.C., Kogut, G.T., Trivedi, M.M.
Format: Article
Language:English
Subjects:
Data mining
Fading
Image segmentation
Information resources
Layout
Object detection
Probability
Robustness
Statistics
Traffic control
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Summary:We present an algorithm for segmentation of traffic scenes that distinguishes moving objects from their moving cast shadows. A fading memory estimator calculates mean and variance of all three color components for each background pixel. Given the statistics for a background pixel, simple rules for calculating its statistics when covered by a shadow are used. Then, MAP classification decisions are made for each pixel. In addition to the color features, we examine the use of neighborhood information to produce smoother classification. We also propose the use of temporal information by modifying class a priori probabilities based on predictions from the previous frame.
ISSN:1051-4651
2831-7475
DOI:10.1109/ICPR.2000.905341