Dynamic Color Flow: A Motion-Adaptive Color Model for Object Segmentation in Video

Abstract

Accurately modeling object colors, and features in general, plays a critical role in video segmentation and analysis. Commonly used color models, such as global Gaussian mixtures, localized Gaussian mixtures, and pixel-wise adaptive ones, often fail to accurately represent the object appearance in complicated scenes, thereby leading to segmentation errors. We introduce a new color model, Dynamic Color Flow, which unlike previous approaches, incorporates motion estimation into color modeling in a probabilistic framework, and adaptively changes model parameters to match the local properties of the motion. The proposed model accurately and reliably describes changes in the scene’s appearance caused by motion across frames. We show how to apply this color model to both foreground and background layers in a balanced way for efficient object segmentation in video. Experimental results show that when compared with previous approaches, our model provides more accurate foreground and background estimations, leading to more efficient video object cutout systems.

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