Shadow removal for pedestrian detection and tracking in indoor environments

Shadow removal for pedestrian detection and tracking in indoor environments This paper presents a method of shadow removal to improve the accuracy of pedestrian detection and tracking in indoor environments. The proposed method can be divided into four steps: building a background model which can be automatically updated, extract moving objects region, eliminating moving objects shadows, classifying and track pedestrians. The background model is built with pixel value and the updating of Gussian. The approach for real time background-foreground extraction is used to extract pedestrian region that may contains multiple shadows. In the gray histogram space, based on the depth value of the gray images, a reasonable threshold is set to remove shadows from various pedestrians. In this work, we propose a methodology using the foreground frames without shadows to detect and track the pedestrians across training datasets. Comparative experimental results show that our method is capable of dealing with shadows and detecting moving pedestrians in cluttered environments. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Shadow removal for pedestrian detection and tracking in indoor environments

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Publisher
Springer US
Copyright
Copyright © 2016 by Springer Science+Business Media New York
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-016-3880-6
Publisher site
See Article on Publisher Site

Abstract

This paper presents a method of shadow removal to improve the accuracy of pedestrian detection and tracking in indoor environments. The proposed method can be divided into four steps: building a background model which can be automatically updated, extract moving objects region, eliminating moving objects shadows, classifying and track pedestrians. The background model is built with pixel value and the updating of Gussian. The approach for real time background-foreground extraction is used to extract pedestrian region that may contains multiple shadows. In the gray histogram space, based on the depth value of the gray images, a reasonable threshold is set to remove shadows from various pedestrians. In this work, we propose a methodology using the foreground frames without shadows to detect and track the pedestrians across training datasets. Comparative experimental results show that our method is capable of dealing with shadows and detecting moving pedestrians in cluttered environments.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Sep 2, 2016

References

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