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Title	City-Scale Vehicle Tracking And Traffic Flow Estimation Using Low Frame-Rate Traffic Cameras
ID_Doc	14210
Authors	Wei P.; Shi H.; Yang J.; Qian J.; Ji Y.; Jiang X.
Year	2019
Published	UbiComp/ISWC 2019- - Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
DOI	http://dx.doi.org/10.1145/3341162.3349336
Abstract	Vehicle flow estimation has many potential smart cities and transportation applications. Many cities have existing camera networks which broadcast image feeds; however, the resolution and frame-rate are too low for existing computer vision algorithms to accurately estimate flow. In this work, we present a computer vision and deep learning framework for vehicle tracking. We demonstrate a novel tracking pipeline which enables accurate flow estimates in a range of environments under low resolution and frame-rate constraints. We demonstrate that our system is able to track vehicles in New York City’s traffic camera video feeds at 1 Hz or lower frame-rate, and produces higher traffic flow accuracy than popular open source tracking frameworks. © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.
Author Keywords	Computer vision; Real-time systems; Traffic flow

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