| Project/Area Number |
16500107
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Nagoya University |
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Principal Investigator |
KATO Jien Nagoya University, Graduate School of Information Science, Associate Professor, 大学院・情報科学研究科, 助教授 (70251882)
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| Co-Investigator(Kenkyū-buntansha) |
WATANABE Toyohide Nagoya University, Graduate School of Information Science, Professor, 大学院・情報科学研究科, 教授 (80093342)
ASAKURA Koichi Nagoya University, The Office of Planning Evaluation, Associate Professor, 評価企画室, 助教授 (80273283)
KOJIRI Tomoko Nagoya University, Information Technology Center, Assistant Professor, 情報連携基盤センター, 助手 (40362298)
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| Project Period (FY) |
2004 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥3,800,000 (Direct Cost: ¥3,800,000)
Fiscal Year 2005: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2004: ¥2,700,000 (Direct Cost: ¥2,700,000)
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| Keywords | Tracking / Intelligent Transportation Systems / Hidden Markov Model / Recognition of road traffic sound / Linear Discrimination Method / Cubic Higher-order Local Auto-Correlation (CHLAC) / In-vehicle camera / Detection of moving objects / 自動車追跡 / 自動交通監視 / 隠れマルコフモデル / マルコフ確率場 / 移動物体追跡 / 信号処理 / 画像と音の融合 |
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| Research Abstract |
Based on the idea that a variety of information useful for realization of Intelligent Transportation Systems (ITS) can be extracted from traffic movies, a lot of research efforts have been deveted to develop vehicle tracking techniques using traffic surveillance sequences. However, because of issues in robustness, the tracking techniques are still difficult to be put to practical use. The purpose of this research is to expand the application scope of the vehicle tracking techniques by greatly enhancing the robustness of them.
In the period of the research supported by Grant-In-Aid for Scientific Research, first we have established a tracking method that integrates low-level tracking process with the high-level tracking process, which enables transformation of target objects to be tracked over time in high-dimensional state space, in Bayes framework to adapt to the influence from rapid changes of lighting conditions and object's motions. By using this method, we have achieved the compati
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bleness between robustness and real-time execution of tracing.
Secondly, within above-mentioned framework, recognition mechanism that functions as low-level tracking has been mainly investigated and implemented. This low-level tracking process can not only work individually as a tracker but can also work jointly with high-level tracking process to make the whole system more reliable. Additionally, (1) to keep the robustness of the system even in case of bad weather, night or occurrence of occlusion (because of overlap of vehicle on images), we have developed a method to detect the passages of vehicles based on recognizing road traffic sound taken by a stereo microphones and proved its effectiveness especially for night-image-sequences ; (2) to obtain necessary information in the situation where road-side-infrastructures are not available, we have investigated and developed a method to detect and track neighboring vehicles by using in-vehicle cameras ; (3) to support safe driving, we have developed the techniques to identify and distinguish vehicles and pedestrian at intersections.
The results related to this research project have been published in several proceedings of international canferences and journals. Less
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