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Found 9 papers, 6 papers with code
Learn to Recover Visible Color for Video Surveillance in a Day
In silicon sensors, the interference between visible and near-infrared (NIR) signals is a crucial problem.
MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation
Urban time series data forecasting featuring significant contributions to sustainable development is widely studied as an essential task of the smart city.
Ranked #1 on
Traffic Prediction
on Beijing Traffic
Real-World Video for Zoom Enhancement based on Spatio-Temporal Coupling
In recent years, single-frame image super-resolution (SR) has become more realistic by considering the zooming effect and using real-world short- and long-focus image pairs.
ST-ExpertNet: A Deep Expert Framework for Traffic Prediction
Recently, forecasting the crowd flows has become an important research topic, and plentiful technologies have achieved good performances.
Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction
Forecasting incident occurrences (e. g. crime, EMS, traffic accident) is a crucial task for emergency service providers and transportation agencies in performing response time optimization and dynamic fleet management.
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction
Nowadays, with the rapid development of IoT (Internet of Things) and CPS (Cyber-Physical Systems) technologies, big spatiotemporal data are being generated from mobile phones, car navigation systems, and traffic sensors.
DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction
Based on this idea, a series of methods have been proposed to address grid-based prediction for citywide crowd and traffic.
VLUC: An Empirical Benchmark for Video-Like Urban Computing on Citywide Crowd and Traffic Prediction
In this study, we publish a new aggregated human mobility dataset generated from a real-world smartphone application and build a standard benchmark for such kind of video-like urban computing with this new dataset and the existing open datasets.
DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events
Therefore in this study, we aim to extract the “deep” trend only from the current momentary observations and generate an accurate prediction for the trend in the short future, which is considered to be an effective way to deal with the event situations.
