Adaptive Color Extraction Employing an Update-type Color Histogram Model under Illumination Change and Its Application to Person Tracking

摘要

本論文は,可変照明下における逐次更新型色ヒストグラムを用いた,人物追跡法を提案する.提案法は,HSV色空間のうちH-S平面上の色ヒストグラムを用いて,対象の色ヒストグラムモデルを取得し,変化する照明のもとでそれを逐次更新しながら対象を追跡する.色モデルを更新する場合,対象色以外の色によって色モデルが発散するという問題がある.提案法では,色モデル上にカーネル関数として非等方性非対称ガウス状分布による重みを設け,逐次更新による色モデルの発散を防ぐ.パン・チルトカメラを用いた色追跡システムを構築し,室内灯の点滅による時間的に不均一な室内照明環境と,日陰が存在する屋外の空間的に不均一な照明環境下で特定色の上着を着用した人物の追跡実験を行い,どちらの環境下においても良好な追跡結果を得た.In this paper, we propose an adaptive color extraction technique employing an update type two-dimensional color histogram model. A two-dimensional color histogram model using an H-S plane in the HSV color space is updated every time, and it extracts objectu27s color that varies under changeable illumination. When updating the color model, color of the background area might be included in a color model as a noise. By such a miss update, a color model will be overflowed with non-objectu27s colors. We define an asymmetric anisotropic Gaussian-like distribution in order to estimate a changeable color area of an object, and employ it as kernel function for providing weight coefficients to the model update. We have developed a color tracking system using a pan-tilt camera, and performed tracking experiments in two different environments, i.e., an indoor environment with changeable illumination and an outdoor environment under sunshine. As a result, the developed system kept tracking an object successfully even though the illumination has changed in both environments.