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1 (MIRU2008) , () EM Automatic Calibration of an In-vehicle Gaze Tracking System Based on the Driver s Gaze Behavior Kenji YAMASHIRO, Tomokazu TAKAHASHI, Ichiro IDE, Hiroshi MURASE, Kazunori HIGUCHI, and Takashi NAITO Graduate School of Information Science, Nagoya University Furo-cho, Chikusa-ku, Nagoya, Aichi, Japan Toyota Central Research & Development Laboratories, Inc {yamashiro,ttakahashi,ide,murase}@murase.m.is.nagoya-u.ac.jp Abstract Many researches to measure and to use driver s gaze directions have been reported to prevent traffic accidents caused by inattentive driving, neglect of safety confirmation, etc. The calibration process of a gaze tracking system is necessary to measure the correct gaze directions. However, several conventional methods for the calibration process have some problems that it causes difficulties for users because they have to gaze at specified points before the gaze directions are measured. In this paper, we propose a method for automatic calibration of a gaze tracking system. We pay attention to the driver s typical gaze behavior, and thus use the rearview and side view mirror positions as the data points for the calibration process. The effectiveness of the proposed method was demonstrated by experiments on gaze direction data while driving local roads. Key words Automatic Calibration, Gaze Tracking System, Driver, Gaze Behavior [] 50

2 FreeGaze [2] [3] EM EM 5

3 3. 2 視線データの絞込み 移動する区間がある 提案手法では 運転中のドライバの視線データのうち 左 停留区間の抽出方法を以下に示す 停留区間では連続する数 レーンから右レーンへの車線変更前および車線変更中のドライ サンプルにわたり視線の移動速度が小さいと考えられる その バの視線データを用いる 車線変更時には ミラーを注視する ため 連続する N 個のサンプル xi (i = n, n +,..., n + N ) を 頻度が増加するためである [4] 例として 約 時間走行した際 含む区間に対して その区間内の 2 つのサンプル間のユーク の全視線データの分布を図 2 に示し そのうち車線変更 4 回 リッド距離の最大値が 閾値 C 以下の場合を停留区間と定義す 分の視線データの分布を図 3 に示す 車線変更時の視線データ る 式 に停留区間の定義式を示す のみに絞ることでミラー注視時の視線方向の推定精度が向上す max (xi xj )T (xi xj ) < C ると考えられる 車線変更の自動検出については ウインカ操 () (i, j = n, n +,..., n + N ) 作と GPS を用いた研究 [5] などがあるため 提案手法では車線 この定義に従って停留区間の視線データを抽出する 例とし 変更は何らかの手段により検出されているものとする て 車線変更時の視線データ 図 3 から停留区間の視線デー タを抽出した結果を図 4 に示す ᣇะ䈱ⷞ ᐲ UDG ᣇะ䈱ⷞ ᐲ UDG 㪇㪅㪌 ᐔ ᣇ ะ ⷞ ᐲ 䊋䉾䉪䊚䊤䊷 㪇㪅㪊 ᱜ㕙 㪇㪅㪈 ฝ䉰䉟䊄䊚䊤䊷 UDG 㪇 㪄㪈 㪄㪇㪅㪏 㪄㪇㪅㪍 㪄 㪄 㪇 㪇㪅㪍 㪇㪅㪏 UDG 㪄㪇㪅㪈 ᐔ ᣇ ะ ⷞ 㪈 ᐲ 㪄 㪄㪇㪅㪊 㪄 図 2 全視線データの分布 㪄㪇㪅㪌 図 4 停留区間の視線データの分布 ᣇะ䈱ⷞ ᐲ UDG 䊋䉾䉪䊚䊤䊷 㪇㪅㪌 3. 4 EM アルゴリズムによるミラー注視時の視線方向推定 視線データの分布からミラー注視時の視線方向を推定する 㪇㪅㪊 車線変更時の視線分布は バックミラー 右サイドミラー 正 ᱜ㕙 㪇㪅㪈 ฝ䉰䉟䊄䊚䊤䊷 㪇 㪄㪈 㪄㪇㪅㪏 㪄㪇㪅㪍 㪄 㪄 㪇㩷 㪇㪅㪍 㪄㪇㪅㪈 㪄 㪇㪅㪏 㪈 面付近に集中する そのため 提案手法では視線データの分布 UDG ᐔ ᣇ ะ ⷞ ᐲ 時の推定視線方向とする が 3 つの正規分布の混合により形成されていると仮定する 混合正規分布に対する EM アルゴリズムは文献 [7] を参照し た EM アルゴリズムを用いて 3 つの正規分布の各平均ベクト ルを求め それらをバックミラー 右サイドミラー 正面注視 㪄㪇㪅㪊 3. 5 較正用パラメータの算出と視線の較正 㪄 計測される視線と実際の正しい視線の間にはずれが存在する このずれを修正するための較正用パラメータを算出する 提案 㪄㪇㪅㪌 手法では 計測される視線と実際の視線の間のずれを平行移動 図 3 車線変更時の視線データの分布 回転 拡大縮小の関係で近似的に補正可能であると仮定する 計測された視線 x, y と較正後の視線 x, y の関係は式 2 で 3. 3 停留区間の視線データの抽出 表される ドライバの視線データのうち ミラーまたは正面を注視して いる区間を停留区間と呼ぶ 停留区間以外の区間はミラー注視 x x w y = H y ただし H = w2 時の視線方向を推定するのに不必要なデータであり むしろ雑 音となって視線方向推定に悪影響を与えると考えられる 停留 区間以外の区間として 具体的には正面とミラーの間を視線が 0 w2 w3 w w4 (2) 0 提案手法では 推定されたミラー注視時の視線方向と実際の 52

4 H w...w x b,y b x r,y r x b,y bx r,y r x b x r x b x r = H y b y r (3) y b y r 4 x, y ˆx, ŷ ˆx x ŷ = H y (4) SeeingMachines FaceLAB [6] 60 FaceLAB EM EM (a) (b) 5 0 N = rad C =0.5 EM ( ) , ( ) , ( ) ,

5 2 rad rad 50 cm 0.5 cm 0.04 rad 70 cm 2.8 cm rad 5 x, y x,y x,y x x y = ABC y (5) cos( π 8 ) sin( π 8 ) 0 A = sin( π 8 ) cos( π 8 ) B = C = rad.5 rad.0 rad.0 rad 0. rad 5 54

6 rad rad m cm rad 0.0 rad, MIST ( [] [2] 2 vol.44, no.4, pp.36 49April [3] FreeGaze HIP July [4] Dario D. SalvucciAndrew Liu The time course of a lane changedriver control and eye-movement behaviortransportation ResearchPart Fvol.5no.2, pp.23 32June [5] 0, vol.7, pp.46 49October [6] Seeing Machines [7] EM vol.2no.5pp October

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