| Title |
Street Object Detection / Tracking For Ai City Traffic Analysis |
| ID_Doc |
43104 |
| Authors |
Wei, Y; Song, NH; Ke, LP; Chang, MC; Lyu, SW |
| Title |
Street Object Detection / Tracking For Ai City Traffic Analysis |
| Year |
2017 |
| Published |
|
| DOI |
|
| 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. |
| Author Keywords |
object detection; multi-object tracking; traffic analysis; smart transportation; AI City |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000464418300276 |
| WoS Category |
Computer Science, Theory & Methods; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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