Precise real‐time obstacle recognition is both vital to vehicle automation and extremely
resource intensive. Current deep‐learning based recognition techniques generally reach …

Crowd counting is a valuable technology for extremely dense scenes in the transportation.
Existing methods generally have higher-order inconsistencies between ground truth density …

In this traffic-scene-modeling study, we propose an image-captioning network which
incorporates element attention into an encoder-decoder mechanism to generate more …

Vehicle detection is a fundamental task in Forward Collision Avoiding Systems (FACS).
Generally, vision-based vehicle detection methods consist of two stages: hypotheses …

From the early stage of robotic applications to current autonomous driving technologies,
environment modeling has been acting as the middleware for connecting perception and …

With the continuous improvement of the economic level of various countries and the
continuous development of urbanization, the problems of urban traffic congestion and traffic …

Scene reconstruction based on image rendering is an indispensable but challenging task in
computer vision and intelligent transportation systems. We propose a framework for …

Spatiotemporal analysis of road scenes is a hot research topic in the communities of
computer vision and intelligent transportation systems. In this paper, we propose a new …

In this paper, a traffic scene construction framework is proposed based on geometric and
semantic analysis of road image sequences using convolutional neural networks (CNNs) …

In this paper, we propose a road scene prediction framework based on the control points of
road boundaries using CNN. Firstly, the image features are extracted and the heatmaps are …