A semantic understanding of road traffic can help people understand road traffic flow
situations and emergencies more accurately and provide a more accurate basis for anomaly …

Abstract The AI City Challenge's seventh edition emphasizes two domains at the intersection
of computer vision and artificial intelligence-retail business and Intelligent Traffic Systems …

City-scale multi-camera vehicle tracking is an important task in the intelligent city and traffic
management. It is quite challenging with large scale variance, frequent occlusion and …

Using generative models to synthesize visual features from semantic distribution is one of
the most popular solutions to ZSL image classification in recent years. The triplet loss (TL) is …

Due to the rapid growth in the number of vehicles over the last decade, there has been a
dramatic increase in demand for highway capacity analysis. Vehicle counting, in particular …

Activity detection is one of the attractive computer vision tasks to exploit the video streams
captured by widely installed cameras. Although achieving impressive performance …

One of the key differences between video and image understanding lies in how to model the
temporal information. Due to the limit of convolution kernel size, most previous methods try …

This paper develops vehicle detection and tracking method for 511 camera networks based
on the spatial-temporal map (STMap) as an add-on toolbox for the traveler information …

Despite the high utility of traffic volume and turning movement data, such data is still hard to
come by for the vast majority of roadways and intersections in nearly every city. Edge …

Deep neural networks are the state of the art for various machine learning problems dealing
with large amounts of rich sensor data. It is often desirable to evaluate these models on …