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 …

The use of computer vision techniques to detect objects in images has grown in recent
years. These techniques are especially useful to automatically extract and analyze …

Abstract The eighth AI City Challenge highlighted the convergence of computer vision and
artificial intelligence in areas like retail warehouse settings and Intelligent Traffic Systems …

Although a significant progress has been witnessed in supervised person re-identification
(re-id), it remains challenging to generalize re-id models to new domains due to the huge …

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 …

Concurrent perception datasets for autonomous driving are mainly limited to frontal view
with sensors mounted on the vehicle. None of them is designed for the overlooked roadside …

In this work, we address the problem of unsupervised domain adaptation for person re-ID
where annotations are available for the source domain but not for target. Previous methods …

This paper uses a graphic engine to simulate a large amount of training data with free
annotations. Between synthetic and real data, there is a two-level domain gap, ie, content …

Any intelligent traffic monitoring system must be able to detect anomalies such as traffic
accidents in real-time. In this paper, we propose a Decision-Tree enabled approach …

Naturalistic driving action recognition (NDAR) has proven to be an effective method for
detecting driver distraction and reducing the risk of traffic accidents. However, the intrusive …