Robustness to changes in illumination conditions as well as viewing perspectives is an important requirement for many computer vision applications. One of the key factors in enhancing the robustness of dynamic scene analysis is that of accurate and reliable means for shadow detection. Shadow detection is critical for correct object detection in image sequences. Many algorithms have been proposed in the literature that deal with shadows. However, a comparative evaluation of the existing approaches isstill lacking. In this paper, the full range of problems underlyingthe shadow detection are identified and discussed. We classify the proposed solutions to this problem using a taxonomy of four main classes, called deterministic model and non-model based and statistical parametric and nonparametric. Novel quantitative (detection and discrimination accuracy) and qualitative metrics (scene and object independence, flexibility to shadow situations and robustness to noise) are proposed to evaluate these classes of algorithms on a benchmark suite of indoor and outdoor videosequences.

Analysis and detection of shadows in video streams: a comparative evaluation / Prati, Andrea; Cucchiara, Rita; I., Mikic; Mm, Trivedi. - STAMPA. - 2:(2001), pp. 571-576. ((Intervento presentato al convegno IEEE-CS Computer Vision and Pattern Recognition conference tenutosi a Kauai, Hawaii, USA nel 8-14 December 2001.

Analysis and detection of shadows in video streams: a comparative evaluation

PRATI, Andrea;CUCCHIARA, Rita;
2001

Abstract

Robustness to changes in illumination conditions as well as viewing perspectives is an important requirement for many computer vision applications. One of the key factors in enhancing the robustness of dynamic scene analysis is that of accurate and reliable means for shadow detection. Shadow detection is critical for correct object detection in image sequences. Many algorithms have been proposed in the literature that deal with shadows. However, a comparative evaluation of the existing approaches isstill lacking. In this paper, the full range of problems underlyingthe shadow detection are identified and discussed. We classify the proposed solutions to this problem using a taxonomy of four main classes, called deterministic model and non-model based and statistical parametric and nonparametric. Novel quantitative (detection and discrimination accuracy) and qualitative metrics (scene and object independence, flexibility to shadow situations and robustness to noise) are proposed to evaluate these classes of algorithms on a benchmark suite of indoor and outdoor videosequences.
IEEE-CS Computer Vision and Pattern Recognition conference
Kauai, Hawaii, USA
8-14 December 2001
2
571
576
Prati, Andrea; Cucchiara, Rita; I., Mikic; Mm, Trivedi
Analysis and detection of shadows in video streams: a comparative evaluation / Prati, Andrea; Cucchiara, Rita; I., Mikic; Mm, Trivedi. - STAMPA. - 2:(2001), pp. 571-576. ((Intervento presentato al convegno IEEE-CS Computer Vision and Pattern Recognition conference tenutosi a Kauai, Hawaii, USA nel 8-14 December 2001.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/308220
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