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Cited by 436

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Improving shadow suppression in moving object detection with HSV color information

Open access
Cucchiara, R., Crana, C., Piccardi, M., Prati, A., Sirotti, S.·2002·ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585)

Abstract

Video-surveillance and traffic analysis systems can be heavily improved using vision-based techniques able to extract, manage and track objects in the scene. However, problems arise due to shadows. In particular, moving shadows can affect the correct localization, measurements and detection of moving objects. This work aims to present a technique for shadow detection and suppression used in a system for moving visual object detection and tracking. The major novelty of the shadow detection technique is the analysis carried out in the HSV color space to improve the accuracy in detecting shadows. Signal processing and optic motivations of the approach proposed are described. The integration and exploitation of the shadow detection module into the system are outlined and experimental results are shown and evaluated.

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Metadata

Authors
Cucchiara, R., Crana, C., Piccardi, M., Prati, A., & Sirotti, S.
Venue
ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585)
Publisher
IEEE
Published
2002-11-13
Pages
334-339
OpenAlex
W2138187652
Cited by
436
References
0
FWCI
6.43
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Improving shadow suppression in moving object detection with HSV color information. https://app.cochat.ai/papers/10.1109/itsc.2001.948679
The paper
Cucchiara, R., Crana, C., Piccardi, M., Prati, A., & Sirotti, S. (2002). Improving shadow suppression in moving object detection with HSV color information. ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585). https://doi.org/10.1109/itsc.2001.948679

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.