The Experts below are selected from a list of 4620 Experts worldwide ranked by ideXlab platform
Peter Paul - One of the best experts on this subject based on the ideXlab platform.
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passenger compartment violation detection in hov hot lanes
2016Co-Authors: Yusuf Artan, Orhan Bulan, Robert P. Loce, Peter PaulAbstract:Due to the high volume of traffic on modern roadways, transportation agencies have proposed high occupancy vehicle (HOV) and high occupancy tolling (HOT) lanes to promote carpooling. Enforcement of the rules of these lanes is currently performed by roadside enforcement officers using visual observation. Officer-based enforcement is, however, known to be inefficient, costly, potentially dangerous, and ultimately ineffective. Violation rates up to 50%–80% have been reported, whereas manual enforcement rates of less than 10% are typical. Near-infrared (NIR) camera systems have been recently proposed to monitor HOV/HOT lanes and enforce the regulations. These camera systems bring an opportunity to automatically determine vehicle occupancy from captured HOV/HOT NIR images. Due to their ability to see through Windshields of vehicles, these cameras also enable enforcement of other passenger compartment violations such as seatbelt violation and driver cell phone usage, in addition to determining vehicle occupancy. In this paper, we propose computer vision methods for detecting vehicle occupancy, seatbelt violation, and driver cell phone usage from NIR images captured from HOV/HOT lanes. Our methods consist of two stages. First, we localize the vehicle's front Windshield and side window from captured HOV/HOT images using the deformable part model (DPM). Next, we define a region of interest in the localized images for each violation type and perform image classification using one of the local aggregation-based image features, i.e., bag-of-visual-words (BOW), vector of locally aggregated descriptors (VLAD), and Fisher vectors (FV), and compare their performances for each case. We also compare the performance of DPM-based detection with the image classification methods for vehicle occupancy and seatbelt violation detection. A data set over 4000 images including front/side view vehicle images with seatbelt and cell phone violations was collected on a public roadway and is used to perform the experiments.
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a machine learning approach to vehicle occupancy detection
2014Co-Authors: Peter Paul, Yusuf Artan, Florent PerronninAbstract:To manage ever increasing traffic volume on modern highways, transportation agencies have introduced special managed lanes where only vehicles with a certain occupancy level are allowed. This encourages highway users to ride together, thus, in theory, more efficiently transporting people through the highway system. In order to be effective, however, adherence to the vehicle occupancy rules has to be enforced. Recent studies have shown that the traditional approach of dispatching traffic law enforcement officers to perform roadside visual inspections is not only expensive and dangerous, but also ineffective for managed lane enforcement. In this paper, the authors describe an image-based machine learning approach for automatic or semi-automatic vehicle occupancy detection. The method localizes Windshield regions by constructing an elastic deformation model from sets of uniquely defined landmark points along the front Windshield. From the localized Windshield region, the method calculates image-level feature representations, which are then applied to a trained classifier for classifying the vehicle into violator and non-violator classes.
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driver cell phone usage detection from hov hot nir images
2014Co-Authors: Yusuf Artan, Orhan Bulan, Robert P. Loce, Peter PaulAbstract:Distracted driving due to cell phone usage is an increasingly costly problem in terms of lost lives and damaged property. Motivated by its impact on public safety and property, several state and federal governments have enacted regulations that prohibit driver mobile phone usage while driving. These regulations have created a need for cell phone usage detection for law enforcement. In this paper, we propose a computer vision based method for determining driver cell phone usage using a near infrared (NIR) camera system directed at the vehicle's front Windshield. The developed method consists of two stages, first, we localize the driver's face region within the front Windshield image using the deformable part model (DPM). Next, we utilize a local aggregation based image classification technique to classify a region of interest (ROI) around the drivers face to detect the cell phone usage. We propose two classification architectures by using full face and half face images for classification and compare their performance in terms of accuracy, specificity, and sensitivity. We also present a comparison of various local aggregation-based image classification methods using bag-of-visual-words (BOW), vector of locally aggregated descriptors (VLAD) and Fisher vectors (FV). A data set of 1500 images was collected on a public roadway and is used to perform the experiments.
Robert P. Loce - One of the best experts on this subject based on the ideXlab platform.
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passenger compartment violation detection in hov hot lanes
2016Co-Authors: Yusuf Artan, Orhan Bulan, Robert P. Loce, Peter PaulAbstract:Due to the high volume of traffic on modern roadways, transportation agencies have proposed high occupancy vehicle (HOV) and high occupancy tolling (HOT) lanes to promote carpooling. Enforcement of the rules of these lanes is currently performed by roadside enforcement officers using visual observation. Officer-based enforcement is, however, known to be inefficient, costly, potentially dangerous, and ultimately ineffective. Violation rates up to 50%–80% have been reported, whereas manual enforcement rates of less than 10% are typical. Near-infrared (NIR) camera systems have been recently proposed to monitor HOV/HOT lanes and enforce the regulations. These camera systems bring an opportunity to automatically determine vehicle occupancy from captured HOV/HOT NIR images. Due to their ability to see through Windshields of vehicles, these cameras also enable enforcement of other passenger compartment violations such as seatbelt violation and driver cell phone usage, in addition to determining vehicle occupancy. In this paper, we propose computer vision methods for detecting vehicle occupancy, seatbelt violation, and driver cell phone usage from NIR images captured from HOV/HOT lanes. Our methods consist of two stages. First, we localize the vehicle's front Windshield and side window from captured HOV/HOT images using the deformable part model (DPM). Next, we define a region of interest in the localized images for each violation type and perform image classification using one of the local aggregation-based image features, i.e., bag-of-visual-words (BOW), vector of locally aggregated descriptors (VLAD), and Fisher vectors (FV), and compare their performances for each case. We also compare the performance of DPM-based detection with the image classification methods for vehicle occupancy and seatbelt violation detection. A data set over 4000 images including front/side view vehicle images with seatbelt and cell phone violations was collected on a public roadway and is used to perform the experiments.
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a machine learning approach for detecting cell phone usage
2015Co-Authors: Robert P. LoceAbstract:Cell phone usage while driving is common, but widely considered dangerous due to distraction to the driver. Because of the high number of accidents related to cell phone usage while driving, several states have enacted regulations that prohibit driver cell phone usage while driving. However, to enforce the regulation, current practice requires dispatching law enforcement officers at road side to visually examine incoming cars or having human operators manually examine image/video records to identify violators. Both of these practices are expensive, difficult, and ultimately ineffective. Therefore, there is a need for a semi-automatic or automatic solution to detect driver cell phone usage. In this paper, we propose a machine-learning-based method for detecting driver cell phone usage using a camera system directed at the vehicle’s front Windshield. The developed method consists of two stages: first, the frontal Windshield region localization using the deformable part model (DPM), next, we utilize Fisher vectors (FV) representation to classify the driver’s side of the Windshield into cell phone usage violation and non-violation classes. The proposed method achieved about 95% accuracy with a data set of more than 100 images with drivers in a variety of challenging poses with or without cell phones.
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driver cell phone usage detection from hov hot nir images
2014Co-Authors: Yusuf Artan, Orhan Bulan, Robert P. Loce, Peter PaulAbstract:Distracted driving due to cell phone usage is an increasingly costly problem in terms of lost lives and damaged property. Motivated by its impact on public safety and property, several state and federal governments have enacted regulations that prohibit driver mobile phone usage while driving. These regulations have created a need for cell phone usage detection for law enforcement. In this paper, we propose a computer vision based method for determining driver cell phone usage using a near infrared (NIR) camera system directed at the vehicle's front Windshield. The developed method consists of two stages, first, we localize the driver's face region within the front Windshield image using the deformable part model (DPM). Next, we utilize a local aggregation based image classification technique to classify a region of interest (ROI) around the drivers face to detect the cell phone usage. We propose two classification architectures by using full face and half face images for classification and compare their performance in terms of accuracy, specificity, and sensitivity. We also present a comparison of various local aggregation-based image classification methods using bag-of-visual-words (BOW), vector of locally aggregated descriptors (VLAD) and Fisher vectors (FV). A data set of 1500 images was collected on a public roadway and is used to perform the experiments.
Yusuf Artan - One of the best experts on this subject based on the ideXlab platform.
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passenger compartment violation detection in hov hot lanes
2016Co-Authors: Yusuf Artan, Orhan Bulan, Robert P. Loce, Peter PaulAbstract:Due to the high volume of traffic on modern roadways, transportation agencies have proposed high occupancy vehicle (HOV) and high occupancy tolling (HOT) lanes to promote carpooling. Enforcement of the rules of these lanes is currently performed by roadside enforcement officers using visual observation. Officer-based enforcement is, however, known to be inefficient, costly, potentially dangerous, and ultimately ineffective. Violation rates up to 50%–80% have been reported, whereas manual enforcement rates of less than 10% are typical. Near-infrared (NIR) camera systems have been recently proposed to monitor HOV/HOT lanes and enforce the regulations. These camera systems bring an opportunity to automatically determine vehicle occupancy from captured HOV/HOT NIR images. Due to their ability to see through Windshields of vehicles, these cameras also enable enforcement of other passenger compartment violations such as seatbelt violation and driver cell phone usage, in addition to determining vehicle occupancy. In this paper, we propose computer vision methods for detecting vehicle occupancy, seatbelt violation, and driver cell phone usage from NIR images captured from HOV/HOT lanes. Our methods consist of two stages. First, we localize the vehicle's front Windshield and side window from captured HOV/HOT images using the deformable part model (DPM). Next, we define a region of interest in the localized images for each violation type and perform image classification using one of the local aggregation-based image features, i.e., bag-of-visual-words (BOW), vector of locally aggregated descriptors (VLAD), and Fisher vectors (FV), and compare their performances for each case. We also compare the performance of DPM-based detection with the image classification methods for vehicle occupancy and seatbelt violation detection. A data set over 4000 images including front/side view vehicle images with seatbelt and cell phone violations was collected on a public roadway and is used to perform the experiments.
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a machine learning approach to vehicle occupancy detection
2014Co-Authors: Peter Paul, Yusuf Artan, Florent PerronninAbstract:To manage ever increasing traffic volume on modern highways, transportation agencies have introduced special managed lanes where only vehicles with a certain occupancy level are allowed. This encourages highway users to ride together, thus, in theory, more efficiently transporting people through the highway system. In order to be effective, however, adherence to the vehicle occupancy rules has to be enforced. Recent studies have shown that the traditional approach of dispatching traffic law enforcement officers to perform roadside visual inspections is not only expensive and dangerous, but also ineffective for managed lane enforcement. In this paper, the authors describe an image-based machine learning approach for automatic or semi-automatic vehicle occupancy detection. The method localizes Windshield regions by constructing an elastic deformation model from sets of uniquely defined landmark points along the front Windshield. From the localized Windshield region, the method calculates image-level feature representations, which are then applied to a trained classifier for classifying the vehicle into violator and non-violator classes.
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driver cell phone usage detection from hov hot nir images
2014Co-Authors: Yusuf Artan, Orhan Bulan, Robert P. Loce, Peter PaulAbstract:Distracted driving due to cell phone usage is an increasingly costly problem in terms of lost lives and damaged property. Motivated by its impact on public safety and property, several state and federal governments have enacted regulations that prohibit driver mobile phone usage while driving. These regulations have created a need for cell phone usage detection for law enforcement. In this paper, we propose a computer vision based method for determining driver cell phone usage using a near infrared (NIR) camera system directed at the vehicle's front Windshield. The developed method consists of two stages, first, we localize the driver's face region within the front Windshield image using the deformable part model (DPM). Next, we utilize a local aggregation based image classification technique to classify a region of interest (ROI) around the drivers face to detect the cell phone usage. We propose two classification architectures by using full face and half face images for classification and compare their performance in terms of accuracy, specificity, and sensitivity. We also present a comparison of various local aggregation-based image classification methods using bag-of-visual-words (BOW), vector of locally aggregated descriptors (VLAD) and Fisher vectors (FV). A data set of 1500 images was collected on a public roadway and is used to perform the experiments.
Weihong Zhang - One of the best experts on this subject based on the ideXlab platform.
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finite element simulation of pmma aircraft Windshield against bird strike by using a rate and temperature dependent nonlinear viscoelastic constitutive model
2014Co-Authors: Jun Wang, Weihong ZhangAbstract:In this study, a nonlinear viscoelastic constitutive model including the rate and temperature effect is developed to describe the mechanical behavior of polymethylmethacrylate (PMMA) material under high speed impact loading. Based on the updated Lagrangian approach, the incremented form of the constitutive model is deduced using the updated Kirchhoff stress tensors and strain tensors. Then this model is implemented with a user subroutine into the explicit dynamic finite element program LS-DYNA to simulate the dynamic behaviors of PMMA aircraft Windshield under high speed bird strike. Numerical results are validated against experimental data and further investigations are carried out to study the influence of environmental temperature, impact location on Windshield and bird impact velocity.
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fe analysis of dynamic response of aircraft Windshield against bird impact
2013Co-Authors: Uzair Ahmed Dar, Weihong ZhangAbstract:Bird impact poses serious threats to military and civilian aircrafts as they lead to fatal structural damage to critical aircraft components. The exposed aircraft components such as Windshields, radomes, leading edges, engine structure, and blades are vulnerable to bird strikes. Windshield is the frontal part of cockpit and more susceptible to bird impact. In the present study, finite element (FE) simulations were performed to assess the dynamic response of Windshield against high velocity bird impact. Numerical simulations were performed by developing nonlinear FE model in commercially available explicit FE solver AUTODYN. An elastic-plastic material model coupled with maximum principal strain failure criterion was implemented to model the impact response of Windshield. Numerical model was validated with published experimental results and further employed to investigate the influence of various parameters on dynamic behavior of Windshield. The parameters include the mass, shape, and velocity of bird, angle of impact, and impact location. On the basis of numerical results, the critical bird velocity and failure locations on Windshield were also determined. The results show that these parameters have strong influence on impact response of Windshield, and bird velocity and impact angle were amongst the most critical factors to be considered in Windshield design.
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high speed bird impact analysis of aircraft Windshield by using a nonlinear viscoelastic model
2013Co-Authors: Ahmed Uzair, Jun Wang, Weihong ZhangAbstract:In this study, a numerical model was established to predict the dynamic response of PMMA based polymeric aircraft Windshield against high speed bird impact. A detailed nonlinear viscoelastic constitutive model with tensile failure criterion was used to predict the damage and failure of Windshield structure. The numerical model was implemented by employing user defined material subroutine (UMAT) in explicit finite element (FE) solver LS-DYNA 3D. Numerical results were validated against experimental data and further investigations were carried out to study the influence of increased bird velocity and impact location on Windshield. On the basis of numerical results, the limiting bird velocity and critical impact location on Windshield were determined. The study will help to optimize the design of Windshields against high speed bird strikes.
Yibing Li - One of the best experts on this subject based on the ideXlab platform.
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energy absorption mechanism of polyvinyl butyral laminated Windshield subjected to head impact experiment and numerical simulations
2016Co-Authors: Tingni Xu, Xiaoqing Xu, Yibing LiAbstract:Abstract In this paper, both experiments and numerical simulations are conducted to investigate the energy absorption mechanism of Polyvinyl butyral (PVB) laminated Windshield. Firstly, a set of experiments on Windshield upon headform impact are conducted at various impact velocities (6.6 m/s–11.2 m/s) and angles (60°–90°). The energy absorption process of Windshield under different impact speeds and angles are investigated. In the meantime, a finite element (FE) model representing the impact process between human head and Windshield is set up and validated by experiment results. The influences of PVB interlayer properties (thickness, Young's modulus and yield stress) on the energy absorption capabilities of Windshield are studied by an evaluation of maximum contact force and head injury criterion (HIC). Results indicate that PVB interlayer plays a significant role in Windshield energy absorption and can be improved with respect to Windshield safety design.
