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• W2809997027 abstract "Smart transportation based on big data traffic analysis is an important component of smart city. With millions of ubiquitous street cameras and intelligent analytic algorithms, public transit systems of the next generation can be safer and smarter. We participated the IEEE Smart World 2017 NVIDIA AI City Challenge which consists of two tracks of contests that serve this spirit. In the AI City Track 1 contest on visual detection, we built a competitive street object detector for vehicle and person localization and classification. In the AI City Track 2 contest on transportation applications, we developed a traffic analysis framework based on vehicle tracking that can assist the surveillance and visualization of the traffic flow. Both developed methods demonstrated practical, and competitive performance when evaluated with state-of-art methods on real-world traffic videos provided in the challenge contest." @default.
• W2809997027 created "2018-07-10" @default.
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• W2809997027 date "2017-08-01" @default.
• W2809997027 modified "2023-09-22" @default.
• W2809997027 title "Street object detection / tracking for AI city traffic analysis" @default.
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• W2809997027 doi "https://doi.org/10.1109/uic-atc.2017.8397669" @default.
• W2809997027 hasPublicationYear "2017" @default.
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