Deep Temporal Color Constancy for AC Light Sources

Jun-Sang Yoo, Chan-Ho Lee, Jong-Ok Kim

Abstract: Most of the lights surrounding our world are artificial lights, whose power is supplied by alternative current (AC). The intensities of these lights are dynamically varying with time. In this paper, we propose a novel deep-learning based method for temporal color constancy. We capture this intensity variation of AC lights using high-speed camera, and use it as a close cue to learn an illuminant chromaticity. While most of the existing methods estimate an illuminant from spatial pixels, the proposed method learns temporal feature via AC flickers of a high-speed video. To effectively learn a temporal feature, the high-speed temporal correlation is fed into the proposed network, and helps it to concentrate on illuminant-attentive regions. As a result, the proposed method works well under complex illuminant environment with ambient lights, which was a very hard problem for existing spatial methods. Experimental results show that the proposed method outperforms all existing methods, and demonstrate that it works very robustly under various illuminant environments.