HMM-based Segmentation of Background, Object and Shadow from Traffic Monitoring Movies

加藤, ジェーン, KATO, Jien, 渡邉, 豊英, WATANABE, Toyohide, 米田, 政明, YONEDA, Masaaki

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HMMに基づく交通監視映像の背景・物体・影の分離手法
画像・図形認識 HMMに基づく交通監視映像の背景・物体・影の分離手法
ガゾウズケイニンシキ HMM ニモトヅクコウツウカンシエイゾウノハイケイブッタイカゲノブンリシュホウ

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Description

The main obstacle to the robustness of car tracking is large shadows of vehicles. Even with a good foreground model, the tracking process is liable to be disrupted by the shadows. This paper proposes an HMM-based foreground-background segmentation method which is capable of modeling shadow as well as foreground and background regions. Unlike many other probabilistic background models, it is not necessary to select the training data since the distributions for different regions can be learnt from an ordinary video sequence. The ambiguity among different regions(categories)is reduced by not only using the temporal continuity constraint for each category, but using jointly intensity and Sobel values, which measure the homogeneity of a region in space, as the observation symbols. This method itself functions a low level tracker. It can be also used as a low level process for an active contour based car tracker. Results on real-world motorway sequences show that using the proposed method, it is possible to accurately segment the image into background, cars and the shadow of cars in real time.

Journal

情報処理学会論文誌

情報処理学会論文誌 42 (1), 1-15, 2001-01

情報処理学会

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