IPSJ SIG Technical Report Vol.2009-CVIM-167 No /6/ EPI Efficient removal of obstacles from car-mounted video data by using spatio-tempo
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1 EPI Efficient removal of obstacles from car-mounted video data by using spatio-temporal analysis Kousuke Kuribayashi, 1 Shintaro Ono, 2 Hiroshi Kawasaki 1 and Katsushi Ikeuchi 2 In this research, the method which can automatically remove objects, such as pedestrians, telegraph poles, roadside trees, etc., from the on-vehicle video camera is proposed; such images are widely used for urban scene modeling (e.g. Google Street View), and a removal of these objects now becomes a critical issue. Since an input data is a video stream and an urban scene is mainly composed of planar surfaces parallel to the street, the method can effectively remove the objects by using the spatiotemporal image analysis. To show the strength of the method, several experiments using real data are conducted, which resulted in a successful removal of complicated objects. 1. Google Street View EPI EPI EPI 2. lazy snapping inpainting 3) 6) 1 Saitama University 2 Univercity of Tokyo 1
2 2.1 1) 2) Li lazy snapping 3) 2.2 Korah 4) Matsushita 5) 2.3 Shiratori 6) 1 Thanda EPI 7) 8) EPI EPI EPI EPI EPI 3.2 EPI EPI EPI EPI (x, y) f=0 = (0, 0) (dx, dy) α (x, y) f=α = (x, y) f=α 1 + (dx, dy) 3 (a) EPI (b) EPI (b) 2
3 上から見た撮影の位置関係 z Z= 背景 カメラ カメラ x y Homography x Z= 背景での撮影範囲撮影した画像 Homography 結果 3pixel 18pixel 10pixel 3pixel 3pixel 8pixel (x,y) 0 =(0, 0) (x,y) α =(10, -3) (x,y) n =(18, 0) 1 Homography 4 4 (c) EPI EPI 6 3pixel 2 3 EPI ( ) (a) EPI 6 EPI 3.4 3
4 (b) EPI Canny 3(b) EPI EPI 8 4 (d) EPI (e) EPI 10 Canny 11 Canny (d) EPI (d) EPI 8 9 EPI 8 10 EPI 11 4
5 情報処理学会研究報告 4. 実 験 4.1 評価実験 室内 本手法の評価のため 室内でモーターを使用しカメラを等速直線運動で動かし撮影した (図 12) 撮影したビデオ映像を用いて 障害物の除去および真値との比較実験を行った 撮 影した動画からキャプチャーした画像が図 13 であり この画像列から作成した EPI が図 図 16 複数デプス除去結果画像 14 である これに対して デプス毎に障害物のマスクと空間フィルタを適用すると図 15 の ようになる 背景の奥ゆきが複数の場合でも障害物が除去できたことが確認できる 障害物 4.2 屋外実験１ レールによる実験 を除去して撮影した画像を用いて計算すると PSNR は 20.3db であった 屋外でレールとドリーを使用し レール上をカメラを手押しで進め 撮影した 撮影し た動画からキャプチャーした画像が図 17 である この場合 カメラとの距離が近い垣根が 障害物であり 奥の壁の下半分がほとんど撮影されていない 撮影した映像にブロックマッ チングを行って時空間ボリュームを構築し 300pixel 目の高さにおける EPI が図 18 である また 障害物は幅だけでなく奥行きもあり撮影位置によって常にデプスが変わり EPI 上で の変化が激しい 図 18 にそのまま補間処理を行うと図 19 になり 障害物が背景より支配 的なため ライン毎のヒストグラムピークで障害物の色が選択されてしまい背景色の選択に 失敗してしまう そこで 障害物をオプティカルフローによりマスクした画像が図 20 である 図 20 から 図 12 複数デプス撮影風景 EPI を作成すると図 21 となる 障害物にマスクをしてしまったため色情報がほとんどない ように見えるが 理論的には 列中に背景のみが 1pixel でも残っており他に画素がなけれ ば採用されるため これに時空間フィルタを適用すると図 22 になる この処理をすべての 高さにおける EPI について行い 元の画像に戻すと図 23 になり 垣根が除去できたことが 確認できる さらにフェンスのあるシーンにおいても障害物除去を行った 入力と結果を図 24 に示す 図 13 フェンスのように細い障害物の除去は一般に困難であるが 正しく除去することが出来てい 複数デプスキャプチャー画像 ることが確認できる 4.3 屋外実験２ 車載映像による実験 図 25 のような全方位カメラ Ladybug212) を計測車両に搭載し 走行しながら撮影を行っ た 全方位カメラだと あらゆる方向の画像が一度に撮影できるため 同じ物体 建物 が 長く撮影されることになり 時空間画像解析にとって都合が良い 撮影した全方位画像を図 26 に示す 図 26 を透視投影変換したものが図 27 である 図 27 図 14 図 13 の 300pixel 目の複数デプス EPI 図 15 図 14 に空間フィルタを適用した複数デプス EPI 5 c 2009 Information Processing Society of Japan
6 情報処理学会研究報告 図 21 障害物をマスクした EPI 図 22 空間フィルタを適用した EPI 図 17 入力キャプチャー画像 図 18 図 17 の 300pixel 目の EPI 図 19 障害物除去に失敗した EPI 図 23 障害物除去結果画像 障害物除去後の EPI を画像に戻すと 図 32 となり 電柱は削除できているものの 電線部 分が残っていることが分かる これに対して 図 30 に対して 空間フィルタのサイズを縦一列 13pixel とした時空間フィ ルタを適用した EPI が図 33 である 先ほどと異なり 黒いノイズなどが無くなっているこ とが分かる この結果復元された画像が図 34 であり 時空間フィルタの使用により 電柱 と電線のそれぞれが除去できたことが確認できる 5. ま と め 図 20 本論文では時空間解析の一つである EPI 解析を利用して障害物の除去に関して述べた 障害物をマスクした画像 の 300pixel 目の高さ および ちょうど電線部分の高さ (f) でにおける EPI が それぞれ まず最初に EPI を作成するにあたり 等速でない状況やカメラの上下振動によるのずれに 図 28 および図 30 である 図 30 を見ると 電線の影響により EPI 全体に不規則なパター 対応し 正しい EPI を作成する手法 および オプティカルフローにより障害物をおおま ンが観測される これに空間フィルタを適用した結果が図 29 および図 31 である 図 31 で かに除去した EPI を作成する手法を紹介した 次に こうして得られた EPI に対して 時 は 白い柱の部分に黒い色が混じるなど うまく電線が除去できてないことが分かる この 空間フィルタによりヒストグラム最頻値による補間を行うことで 効率よく障害物を除去す 6 c 2009 Information Processing Society of Japan
7 情報処理学会研究報告 EPI 画像 障害物除去後の EPI 図 26 入力画像 入力全方位画像 障害物除去後 図 24 フェンス除去 図 27 透視投影変換画像 4) C. Rasmussen T. Korah Spatiotemporal inpainting for recovering texture maps of partially occluded building facades In Proceedings of the IEEE ICIP. 5) Yasuyuki Matsushita, Ko Nishino, Katsushi Ikeuchi, Masao Sakauchi, Illumination Normalization with Time-Dependent Intrinsic Images for Video Surveillance, IEEE Trans. Pattern Anal. Mach. Intell., 26(10): (2004). 6) Takaaki Shiratori, Yasuyuki Matsushita, Sing Bing Kang, and Xiaoou Tang, Video completion by motion field transfer, In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 1, pages , June ) Thanda Oo, 川崎洋, 大澤裕, 池内克史, Separation of Reflection and Transparency Based on Spatiotemporal Analysis for Outdoor Scene, IPSJ Vol. 2(2006) pp 図 25 Ladybug2 と計測車 る手法を提案した 最後に 屋内 屋外で撮影したデータを用いて実験を行ったところ 垣 根などの複雑な形状や 移動する物体について正しく障害物除去できることを確認できた 参 考 文 献 1) 小幡 恭久 剣持 雪子 小谷 一孔 画像のフラクタル性を活用した局所的な画像推定 法による画像復元手法 PRMU pp ) 榎本暁人, 斎藤英雄, 複数のハンディカメラを利用した Diminished Reality, MIRU2007 3) Yin Li, Jian Sun, Chi-Keung Tang and Heung-Yeung Shum, Lazy Snapping, SIGGRAPH 2004, Vol. 23, pp c 2009 Information Processing Society of Japan
8 pixel EPI EPI 33 (f) EPI 30 (f) EPI 31 (f) EPI 32 8),,,,,, PRMU97-126, pp (1997). 9) EPI 34 D-II, No.6, pp , ),, 7 ITS, Dec pp ),,, Spatio-temporal volume, MIRU ) P.G.R.Inc.:Ladybug 8
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