Street object detection / tracking for AI city traffic analysis
| Autor: | Siwei Lyu, Ming-Ching Chang, Lipeng Ke, Yi Wei, Nenghui Song |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Traffic analysis
Vehicle tracking system business.industry Computer science Big data Real-time computing 02 engineering and technology 010501 environmental sciences CONTEST Traffic flow 01 natural sciences GeneralLiterature_MISCELLANEOUS Object detection Visualization Smart city 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business 0105 earth and related environmental sciences |
| Zdroj: | SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI Web of Science |
| DOI: | 10.1109/uic-atc.2017.8397669 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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