46,882 research outputs found
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
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
Measuring traffic flow and lane changing from semi-automatic video processing
Comprehensive databases are needed in order to extend our knowledge on the behavior of vehicular traffic. Nevertheless data coming from common traffic detectors is incomplete. Detectors only provide vehicle count, detector occupancy and speed at discrete locations. To enrich these databases additional measurements from other data sources, like video recordings, are used. Extracting data from videos by actually watching the entire length of the recordings and manually counting is extremely time-consuming. The alternative is to set up an automatic video detection system. This is also costly in terms of money and time, and generally does not pay off for sporadic usage on a pilot test. An adaptation of the semi-automatic video processing methodology proposed by Patire (2010) is presented here. It makes possible to count flow and lane changes 90% faster than
actually counting them by looking at the video. The method consists in selecting some specific lined pixels in the video, and converting them into a set of space – time images. The manual time is only spent in counting from these images. The method is adaptive, in the sense that the counting is always done at the maximum speed, not constrained by the video playback speed. This allows going faster when there are a few counts and slower when a lot of counts happen. This methodology has been used for measuring off-ramp flows and lane changing at several locations in the B-23 freeway (Soriguera & Sala, 2014). Results show that, as long as the video recordings fulfill some minimum requirements in framing and quality, the method is easy to use, fast and reliable. This method is intended for research purposes,
when some hours of video recording have to be analyzed, not for long term use in a Traffic Management Center.Postprint (published version
Available seat counting in public rail transport
Surveillance cameras are found almost everywhere today, including vehicles for public transport. A lot of research has already been done on video analysis in open spaces. However, the conditions in a vehicle for public transport differ from these in open spaces, as described in detail in this paper. A use case described in this paper is on counting the available seats in a vehicle using surveillance cameras. We propose an algorithm based on Laplace edge detection, combined with background subtraction
Learned Scalable Video Coding For Humans and Machines
Video coding has traditionally been developed to support services such as
video streaming, videoconferencing, digital TV, and so on. The main intent was
to enable human viewing of the encoded content. However, with the advances in
deep neural networks (DNNs), encoded video is increasingly being used for
automatic video analytics performed by machines. In applications such as
automatic traffic monitoring, analytics such as vehicle detection, tracking and
counting, would run continuously, while human viewing could be required
occasionally to review potential incidents. To support such applications, a new
paradigm for video coding is needed that will facilitate efficient
representation and compression of video for both machine and human use in a
scalable manner. In this manuscript, we introduce the first end-to-end
learnable video codec that supports a machine vision task in its base layer,
while its enhancement layer supports input reconstruction for human viewing.
The proposed system is constructed based on the concept of conditional coding
to achieve better compression gains. Comprehensive experimental evaluations
conducted on four standard video datasets demonstrate that our framework
outperforms both state-of-the-art learned and conventional video codecs in its
base layer, while maintaining comparable performance on the human vision task
in its enhancement layer. We will provide the implementation of the proposed
system at www.github.com upon completion of the review process.Comment: 14 pages, 16 figure
An Intelligent Monitoring System of Vehicles on Highway Traffic
Vehicle speed monitoring and management of highways is the critical problem
of the road in this modern age of growing technology and population. A poor
management results in frequent traffic jam, traffic rules violation and fatal
road accidents. Using traditional techniques of RADAR, LIDAR and LASAR to
address this problem is time-consuming, expensive and tedious. This paper
presents an efficient framework to produce a simple, cost efficient and
intelligent system for vehicle speed monitoring. The proposed method uses an HD
(High Definition) camera mounted on the road side either on a pole or on a
traffic signal for recording video frames. On the basis of these frames, a
vehicle can be tracked by using radius growing method, and its speed can be
calculated by calculating vehicle mask and its displacement in consecutive
frames. The method uses pattern recognition, digital image processing and
mathematical techniques for vehicle detection, tracking and speed calculation.
The validity of the proposed model is proved by testing it on different
highways.Comment: 5 page
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