Abstract
In this paper, we propose an adaptive approach to the detection of moving shadows by employing local neighboring information. The process of the proposed approach is mainly operated by three steps: the first step is to detect the candidate shadows by RGB ratio; the second step is to extract partial accurate shadows in order to estimate accurate threshold parameters of shadow detectors; the final step is to utilize three detectors to detect real shadows from candidate shadows. The main contributions of this paper include two parts: an effective method of candidate shadows detection is presented; an adaptive mechanism of estimating threshold parameters is designed. Moreover, three detectors that consist of color, texture and gradient features are jointly utilized to detect shadows at pixel-level. Experimental results on a benchmark suit of indoor and outdoor video sequences demonstrated the proposed approach’s effectiveness.
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The Test Sequences are from: https://sourceforge.net/projects/arma/files/
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Wang, B., Yuan, Y., Zhao, Y., Zou, W. (2017). Adaptive Moving Shadows Detection Using Local Neighboring Information. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10117. Springer, Cham. https://doi.org/10.1007/978-3-319-54427-4_38
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DOI: https://doi.org/10.1007/978-3-319-54427-4_38
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