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Yunpeng Wang
Researcher at Beihang University
Publications - 152
Citations - 6832
Yunpeng Wang is an academic researcher from Beihang University. The author has contributed to research in topics: Traffic flow & Vehicular ad hoc network. The author has an hindex of 31, co-authored 144 publications receiving 4499 citations. Previous affiliations of Yunpeng Wang include Chinese Ministry of Public Security.
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Car Detection from Low-Altitude UAV Imagery with the Faster R-CNN
TL;DR: The framework of Faster R-CNN for car detection from low-altitude UAV imagery captured over signalized intersections is extended and guided to guide the readers to choose the best vehicle detection framework according to their applications.
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Synergistic effects of the built environment and commuting programs on commute mode choice
TL;DR: In this paper, the authors apply a gradient boosting logit model to examine the influences of BE characteristics at both residential and workplace locations and commuting programs (transit/vanpooling subsidies and parking provision) on commute mode choice.
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Joint analysis of the spatial impacts of built environment on car ownership and travel mode choice
TL;DR: A multilevel integrated multinomial logit (MNL) and structural equation model (SEM) approach was employed to jointly explore the impacts of the built environment on car ownership and travel mode choice and results suggest that application of the multileVEL integrated MNL and SEM approach obtains significant improvements over other models.
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A Hybrid Vehicle Detection Method Based on Viola-Jones and HOG + SVM from UAV Images
TL;DR: A comprehensive evaluation shows that the switching strategy, combined with the road orientation adjustment method, can significantly improve the efficiency and effectiveness of the vehicle detection from UAV images.
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Pedestrian Detection and Tracking from Low-Resolution Unmanned Aerial Vehicle Thermal Imagery
TL;DR: This research proposes an approach for pedestrian tracking that employs the feature tracker with the update of detected pedestrian location to track pedestrian objects from the registered videos and extracts the motion trajectory data.