Attention-guided object and action recognition based on probabilistic learning and feature boosting for understanding human-object interaction
| Project/Area Number |
23500188
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Soka University |
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Principal Investigator |
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| Project Period (FY) |
2011 – 2013
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| Project Status |
Completed (Fiscal Year 2013)
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| Budget Amount *help |
¥5,330,000 (Direct Cost: ¥4,100,000、Indirect Cost: ¥1,230,000)
Fiscal Year 2013: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2012: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2011: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
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| Keywords | 注意 / 確率的学習 / ブースティング / 物体認識 / アクション認識 / コンテキスト / 意味ネットワーク / 確率的推論 / 人工知能 / 機械学習 / パターン認識 / カテゴリゼーション / コンピュータビジョン / 選択的注意 / 知能ロボティクス |
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| Research Abstract |
This research proposed probabilistic methods of attention-guided object recognition and object-oriented action recognition for understanding human-object interaction. In the proposed methods, attention-guided object recognition is performed in the context of co-occurring objects by using a classification tree which is learned based on the probabilistic latent component tree analysis and feature boosting. Also object-oriented action recognition is performed in the mutual contexts of objects and actions by using a probabilistic semantic network of visual motion classes and their semantic tags which is learned based on the incremental probabilistic latent component analysis. It was shown that the proposed method achieved high recognition accuracy through experiments using image data sets of plural object categories and also a set of video clips of object-oriented actions captured by a Kinect sensor mounted on a robot.
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Report
(4 results)
Research Products
(25 results)
