Abstract—This paper presents a new shadow detecting method for silhouette extraction of a person in gray-level video sequences. We use a shadow evaluator to verify each raw shadow pixel that was detected by Gaussian distribution analysis. The evaluator considers a raw shadow pixel initially to be a
fake shadow pixel, and marks it as a
silhouette pixel if it is enclosed or semi-enclosed by
moving occlusion boundaries of a person. Those were extracted by subtracting edges in the current frame from edges of the background. We also propose a silhouette compensation technique to recover some missing (i.e. removed) silhouette pixels by using a similarity criterion between silhouette pixels and their neighbors. Experimental results show us that the proposed algorithm keeps a silhouette of a person more accurate compared to other methods. Methods advocated by other researchers in YUV or RGB color space, typically remove silhouette pixels as shadow if the color of these pixels is similar to that of the surrounding background.
Index Terms—Shadow removal, silhouette detection, shadow
evaluator, silhouette compensation.
The authors were with the University of Auckland, New Zealand (e-mail:
wzhengping@gmail.com).
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Cite:Zhengping Wang, Bok-Suk Shin, and Reinhard Klette, "Accurate Silhouette Extraction of a Person in Video Data by Shadow Evaluation," International Journal of Computer Theory and Engineering vol. 6, no. 6, pp. 476-483, 2014.
