Shadow Detection: A Survey and Comparative Evaluation of Recent Methods
Andres Sanin, Conrad Sanderson, Brian C. Lovell

TL;DR
This survey compares recent shadow detection methods, evaluating their performance and practical usefulness, highlighting their strengths, weaknesses, and applicability in various scenarios.
Contribution
The paper provides a comprehensive taxonomy and comparative analysis of recent shadow detection techniques, including a novel evaluation approach using tracking performance.
Findings
Geometry-based method is simple but not generalizable.
Chromacity method is fast but sensitive to noise.
Physical and texture-based methods offer improved accuracy.
Abstract
This paper presents a survey and a comparative evaluation of recent techniques for moving cast shadow detection. We identify shadow removal as a critical step for improving object detection and tracking. The survey covers methods published during the last decade, and places them in a feature-based taxonomy comprised of four categories: chromacity, physical, geometry and textures. A selection of prominent methods across the categories is compared in terms of quantitative performance measures (shadow detection and discrimination rates, colour desaturation) as well as qualitative observations. Furthermore, we propose the use of tracking performance as an unbiased approach for determining the practical usefulness of shadow detection methods. The evaluation indicates that all shadow detection approaches make different contributions and all have individual strength and weaknesses. Out of the…
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