23,616 research outputs found
FARSEC: A Reproducible Framework for Automatic Real-Time Vehicle Speed Estimation Using Traffic Cameras
Estimating the speed of vehicles using traffic cameras is a crucial task for
traffic surveillance and management, enabling more optimal traffic flow,
improved road safety, and lower environmental impact. Transportation-dependent
systems, such as for navigation and logistics, have great potential to benefit
from reliable speed estimation. While there is prior research in this area
reporting competitive accuracy levels, their solutions lack reproducibility and
robustness across different datasets. To address this, we provide a novel
framework for automatic real-time vehicle speed calculation, which copes with
more diverse data from publicly available traffic cameras to achieve greater
robustness. Our model employs novel techniques to estimate the length of road
segments via depth map prediction. Additionally, our framework is capable of
handling realistic conditions such as camera movements and different video
stream inputs automatically. We compare our model to three well-known models in
the field using their benchmark datasets. While our model does not set a new
state of the art regarding prediction performance, the results are competitive
on realistic CCTV videos. At the same time, our end-to-end pipeline offers more
consistent results, an easier implementation, and better compatibility. Its
modular structure facilitates reproducibility and future improvements
Fine-grained traffic state estimation and visualisation
Tools for visualising the current traffic state are used by local authorities for strategic monitoring of the traffic network and by everyday users for planning their journey. Popular visualisations include those provided by Google Maps and by Inrix. Both employ a traffic lights colour-coding system, where roads on a map are coloured green if traffic is flowing normally and red or black if there is congestion. New sensor technology, especially from wireless sources, is allowing resolution down to lane level. A case study is reported in which a traffic micro-simulation test bed is used to generate high-resolution estimates. An interactive visualisation of the fine-grained traffic state is presented. The visualisation is demonstrated using Google Earth and affords the user a detailed three-dimensional view of the traffic state down to lane level in real time
Understanding Traffic Density from Large-Scale Web Camera Data
Understanding traffic density from large-scale web camera (webcam) videos is
a challenging problem because such videos have low spatial and temporal
resolution, high occlusion and large perspective. To deeply understand traffic
density, we explore both deep learning based and optimization based methods. To
avoid individual vehicle detection and tracking, both methods map the image
into vehicle density map, one based on rank constrained regression and the
other one based on fully convolution networks (FCN). The regression based
method learns different weights for different blocks in the image to increase
freedom degrees of weights and embed perspective information. The FCN based
method jointly estimates vehicle density map and vehicle count with a residual
learning framework to perform end-to-end dense prediction, allowing arbitrary
image resolution, and adapting to different vehicle scales and perspectives. We
analyze and compare both methods, and get insights from optimization based
method to improve deep model. Since existing datasets do not cover all the
challenges in our work, we collected and labelled a large-scale traffic video
dataset, containing 60 million frames from 212 webcams. Both methods are
extensively evaluated and compared on different counting tasks and datasets.
FCN based method significantly reduces the mean absolute error from 10.99 to
5.31 on the public dataset TRANCOS compared with the state-of-the-art baseline.Comment: Accepted by CVPR 2017. Preprint version was uploaded on
http://welcome.isr.tecnico.ulisboa.pt/publications/understanding-traffic-density-from-large-scale-web-camera-data
Traffic Danger Recognition With Surveillance Cameras Without Training Data
We propose a traffic danger recognition model that works with arbitrary
traffic surveillance cameras to identify and predict car crashes. There are too
many cameras to monitor manually. Therefore, we developed a model to predict
and identify car crashes from surveillance cameras based on a 3D reconstruction
of the road plane and prediction of trajectories. For normal traffic, it
supports real-time proactive safety checks of speeds and distances between
vehicles to provide insights about possible high-risk areas. We achieve good
prediction and recognition of car crashes without using any labeled training
data of crashes. Experiments on the BrnoCompSpeed dataset show that our model
can accurately monitor the road, with mean errors of 1.80% for distance
measurement, 2.77 km/h for speed measurement, 0.24 m for car position
prediction, and 2.53 km/h for speed prediction.Comment: To be published in proceedings of Advanced Video and Signal-based
Surveillance (AVSS), 2018 15th IEEE International Conference on, pp. 378-383,
IEE
FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras
In this paper, we develop deep spatio-temporal neural networks to
sequentially count vehicles from low quality videos captured by city cameras
(citycams). Citycam videos have low resolution, low frame rate, high occlusion
and large perspective, making most existing methods lose their efficacy. To
overcome limitations of existing methods and incorporate the temporal
information of traffic video, we design a novel FCN-rLSTM network to jointly
estimate vehicle density and vehicle count by connecting fully convolutional
neural networks (FCN) with long short term memory networks (LSTM) in a residual
learning fashion. Such design leverages the strengths of FCN for pixel-level
prediction and the strengths of LSTM for learning complex temporal dynamics.
The residual learning connection reformulates the vehicle count regression as
learning residual functions with reference to the sum of densities in each
frame, which significantly accelerates the training of networks. To preserve
feature map resolution, we propose a Hyper-Atrous combination to integrate
atrous convolution in FCN and combine feature maps of different convolution
layers. FCN-rLSTM enables refined feature representation and a novel end-to-end
trainable mapping from pixels to vehicle count. We extensively evaluated the
proposed method on different counting tasks with three datasets, with
experimental results demonstrating their effectiveness and robustness. In
particular, FCN-rLSTM reduces the mean absolute error (MAE) from 5.31 to 4.21
on TRANCOS, and reduces the MAE from 2.74 to 1.53 on WebCamT. Training process
is accelerated by 5 times on average.Comment: Accepted by International Conference on Computer Vision (ICCV), 201
Spatial inference of traffic transition using micro-macro traffic variables
This paper proposes an online traffic inference algorithm for road segments in which local traffic information cannot be directly observed. Using macro-micro traffic variables as inputs, the algorithm consists of three main operations. First, it uses interarrival time (time headway) statistics from upstream and downstream locations to spatially infer traffic transitions at an unsupervised piece of segment. Second, it estimates lane-level flow and occupancy at the same unsupervised target site. Third, it estimates individual lane-level shockwave propagation times on the segment. Using real-world closed-circuit television data, it is shown that the proposed algorithm outperforms previously proposed methods in the literature
Regional Data Archiving and Management for Northeast Illinois
This project studies the feasibility and implementation options for establishing a regional data archiving system to help monitor
and manage traffic operations and planning for the northeastern Illinois region. It aims to provide a clear guidance to the
regional transportation agencies, from both technical and business perspectives, about building such a comprehensive
transportation information system. Several implementation alternatives are identified and analyzed. This research is carried
out in three phases.
In the first phase, existing documents related to ITS deployments in the broader Chicago area are summarized, and a
thorough review is conducted of similar systems across the country. Various stakeholders are interviewed to collect
information on all data elements that they store, including the format, system, and granularity. Their perception of a data
archive system, such as potential benefits and costs, is also surveyed. In the second phase, a conceptual design of the
database is developed. This conceptual design includes system architecture, functional modules, user interfaces, and
examples of usage. In the last phase, the possible business models for the archive system to sustain itself are reviewed. We
estimate initial capital and recurring operational/maintenance costs for the system based on realistic information on the
hardware, software, labor, and resource requirements. We also identify possible revenue opportunities.
A few implementation options for the archive system are summarized in this report; namely:
1. System hosted by a partnering agency
2. System contracted to a university
3. System contracted to a national laboratory
4. System outsourced to a service provider
The costs, advantages and disadvantages for each of these recommended options are also provided.ICT-R27-22published or submitted for publicationis peer reviewe
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