394 research outputs found
Comprehensive Survey and Analysis of Techniques, Advancements, and Challenges in Video-Based Traffic Surveillance Systems
The challenges inherent in video surveillance are compounded by a several factors, like dynamic lighting conditions, the coordination of object matching, diverse environmental scenarios, the tracking of heterogeneous objects, and coping with fluctuations in object poses, occlusions, and motion blur. This research endeavor aims to undertake a rigorous and in-depth analysis of deep learning- oriented models utilized for object identification and tracking. Emphasizing the development of effective model design methodologies, this study intends to furnish a exhaustive and in-depth analysis of object tracking and identification models within the specific domain of video surveillance
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
DeepSignals: Predicting Intent of Drivers Through Visual Signals
Detecting the intention of drivers is an essential task in self-driving,
necessary to anticipate sudden events like lane changes and stops. Turn signals
and emergency flashers communicate such intentions, providing seconds of
potentially critical reaction time. In this paper, we propose to detect these
signals in video sequences by using a deep neural network that reasons about
both spatial and temporal information. Our experiments on more than a million
frames show high per-frame accuracy in very challenging scenarios.Comment: To be presented at the IEEE International Conference on Robotics and
Automation (ICRA), 201
Cost Estimate Modeling of Transportation Management Plans for Highway Projects, Research Report 11-24
Highway rehabilitation and reconstruction projects frequently cause road congestion and increase safety concerns while limiting access for road users. State Transportation Agencies (STAs) are challenged to find safer and more efficient ways to renew deteriorating roadways in urban areas. To better address the work zone issues, the Federal Highway Administration published updates to the Work Zone Safety and Mobility Rule. All state and local governments receiving federal aid funding were required to comply with the provisions of the rule no later than October 12, 2007. One of the rule’s major elements is to develop and implement Transportation Management Plans (TMPs). Using well-developed TMP strategies, work zone safety and mobility can be enhanced while road user costs can be minimized. The cost of a TMP for a road project is generally considered a high-cost item and, therefore, must be quantified. However, no tools or systematic modeling methods are available to assist agency engineers with TMP cost estimating. This research included reviewing TMP reports for recent Caltrans projects regarding state-of-the-art TMP practices and input from the district TMP traffic engineers. The researchers collected Caltrans highway project data regarding TMP cost estimating. Then, using Construction Analysis for Pavement Rehabilitation Strategies (CA4PRS) software, the researchers performed case studies. Based on the CA4PRS outcomes of the case studies, a TMP strategy selection and cost estimate (STELCE) model for Caltrans highway projects was proposed. To validate the proposed model, the research demonstrated an application for selecting TMP strategies and estimating TMP costs. Regarding the model’s limitation, the proposed TMP STELCE model was developed based on Caltrans TMP practices and strategies. Therefore, other STAs might require adjustments and modifications, reflecting their TMP processes, before adopting this model. Finally, the authors recommended that a more detailed step-by-step TMP strategy selection and cost estimate process be included in the TMP guidelines to improve the accuracy of TMP cost estimates
EVALUATION OF A COMPUTER VISION TRAFFIC SURVEILLANCE SYSTEM
This thesis presents an evaluation of the accuracy of a novel computer vision traffic sensor - developed by the Clemson University Electrical and Civil Engineering Departments - capable of collecting a variety of traffic parameters. More specific, the thesis examines how the camera height and distance from the travel way affects the accuracy. The details of the quantitative and qualitative evaluations used to validate the system are provided. The parameters chosen to evaluate were volume, vehicle classification, and speed. Experimental results of cameras mounted at heights of 20 and 30 feet and a lateral distance of 10 and 20 feet show accuracy as high as 98 percent for volume and 99 percent for vehicle classification. Results also showed discrepancies in speeds as low as 0.031 miles per hour. Some issues which affected the accuracy were shadows, occlusions, and double counting caused by coding detection errors
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