The Experts below are selected from a list of 16998 Experts worldwide ranked by ideXlab platform
Jing Chen - One of the best experts on this subject based on the ideXlab platform.
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A MACHINE LEARNING FRAMEWORK FOR REAL-TIME Traffic Density DETECTION
International Journal of Pattern Recognition and Artificial Intelligence, 2020Co-Authors: Jing Chen, Zhidong LiAbstract:Traffic flow information can be employed in an intelligent transportation system to detect and manage Traffic congestion. One of the key elements in determining the Traffic flow information is Traffic Density estimation. The goal of Traffic Density estimation is to determine the Density of vehicles on a given road from loop detectors, Traffic radars, or surveillance cameras. However, due to the inflexibility of deploying loop detectors and Traffic radars, there is a growing trend of using video-content-understanding technique to determine the Traffic flow from a surveillance camera. But difficulties arise when attempting to do this in real-time under changing illumination and weather conditions as well as heavy Traffic congestions. In this paper, we attempt to address the problem of real-time Traffic Density estimation by using a stochastic model called Hidden Markov Models (HMM) to probabilistically determine the Traffic Density state. Choosing a good set of model parameters for HMMs has a significant impact on the accuracy of Traffic Density estimation. Thus, we propose a novel feature extraction scheme to represent Traffic Density, and a novel approach to initialize and construct the HMMs by using an unsupervised clustering technique called AutoClass. We show through extensive experiments that our proposed real-time algorithm achieves an average Traffic Density estimation accuracy of 96.6% over various different illumination and weather conditions.
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AVSS - Traffic Density Estimation with On-line SVM Classifier
2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009Co-Authors: Thanes Wassantachat, Jing Chen, Zhidong Li, Yang WangAbstract:Information on the vehicular Traffic Density in an intelligent transport system (ITS) is presently obtained mainly through loop detectors (LD), Traffic radars and surveillance cameras. However, the difficulties and cost of installing loop detectors and Traffic radars tend to be significant. Currently, a more advanced method of circumventing this is to develop a sort of virtual loop detector (VLD) by using video content understanding technology to simulate behavior of a loop detector and to further estimate the Traffic flow from a surveillance camera. Such a virtual loop detector that requires supervised training with human intervention for its setup. Difficulties also arise when attempting to obtain a reliable and real-time VLD under different illumination, weather conditions and static shadows. In this paper, we study the effectiveness of texture features in describing the Traffic Density, and propose a real-time VLD based on on-line SVM classifier and a background modeling technique (OSVM-BG) to estimate the Traffic Density information probabilistically and automatically. The system uses feedback from background modeling to train and update its SVM kernel to self-adapt to various lighting environments. Experimental results show that the system outperforms an existing algorithm and achieves an average accuracy of 89.43% under various illumination changes, weather conditions and especially changing static shadows in daytime.
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Traffic Density Estimation with On-line SVM Classifier
2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, 2009Co-Authors: Thanes Wassantachat, Jing Chen, Zhidong Li, Yang WangAbstract:Information on the vehicular Traffic Density in an intelligent transport system (ITS) is presently obtained mainly through loop detectors (LD), Traffic radars and surveillance cameras. However, the difficulties and cost of installing loop detectors and Traffic radars tend to be significant. Currently, a more advanced method of circumventing this is to develop a sort of virtual loop detector (VLD) by using video content understanding technology to simulate behavior of a loop detector and to further estimate the Traffic flow from a surveillance camera. Such a virtual loop detector that requires supervised training with human intervention for its setup. Difficulties also arise when attempting to obtain a reliable and real-time VLD under different illumination, weather conditions and static shadows. In this paper, we study the effectiveness of texture features in describing the Traffic Density, and propose a real-time VLD based on on-line SVM classifier and a background modeling technique (OSVM-BG) to estimate the Traffic Density information probabilistically and automatically. The system uses feedback from background modeling to train and update its SVM kernel to self-adapt to various lighting environments. Experimental results show that the system outperforms an existing algorithm and achieves an average accuracy of 89.43% under various illumination changes, weather conditions and especially changing static shadows in daytime.
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DICTA - On Traffic Density Estimation with a Boosted SVM Classifier
2008 Digital Image Computing: Techniques and Applications, 2008Co-Authors: Zhidong Li, Jing Chen, Thanes WassantachatAbstract:Traffic Density and flow are important inputs for an intelligent transport system (ITS) to better manage Traffic congestion. Presently, this is obtained through loop detectors (LD), Traffic radars and surveillance cameras. However, installing loop detectors and Traffic radars tends to be difficult and costly. Currently, a more popular way of circumventing this is to develop a sort of virtual loop detector (VLD) by using video content understanding technology to simulate behavior of a loop detector and to further estimate the Traffic flow from a surveillance camera. But difficulties arise when attempting to obtain a reliable and real-time VLD under changing illumination and weather conditions. In this paper, we study the efficiency of using some informative features and the different combinations of the features in describing the Traffic Density, and propose a real-time VLD by using a boosted SVM classifier to probabilistically determine the Traffic Density state. We show through extensive experiments that our proposed real-time VLD achieves an average accuracy at around 95% under various different illumination and weather conditions in daytime.
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On Traffic Density Estimation with a Boosted SVM Classifier
2008 Digital Image Computing: Techniques and Applications, 2008Co-Authors: Zhidong Li, Jing Chen, Thanes WassantachatAbstract:Traffic Density and flow are important inputs for an intelligent transport system (ITS) to better manage Traffic congestion. Presently, this is obtained through loop detectors (LD), Traffic radars and surveillance cameras. However, installing loop detectors and Traffic radars tends to be difficult and costly. Currently, a more popular way of circumventing this is to develop a sort of virtual loop detector (VLD) by using video content understanding technology to simulate behavior of a loop detector and to further estimate the Traffic flow from a surveillance camera. But difficulties arise when attempting to obtain a reliable and real-time VLD under changing illumination and weather conditions. In this paper, we study the efficiency of using some informative features and the different combinations of the features in describing the Traffic Density, and propose a real-time VLD by using a boosted SVM classifier to probabilistically determine the Traffic Density state. We show through extensive experiments that our proposed real-time VLD achieves an average accuracy at around 95% under various different illumination and weather conditions in daytime.
L.g. Malik - One of the best experts on this subject based on the ideXlab platform.
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Acoustic Signature-based Vehicular Traffic Density State Estimation in Developing Regions
International Journal of Vehicle Noise and Vibration, 2016Co-Authors: Prashant Borkar, Milindkumar V. Sarode, L.g. MalikAbstract:In developing regions like Asia, where the Traffic conditions are chaotic and non-lane driven, the intrusive techniques may be inapplicable. The vehicular acoustic signals and the occurrence and mixture weighting of these signals are determined by the prevalent Traffic Density state condition. This research work considers the problem of vehicular Traffic Density state estimation, based on the information present in the acoustic signal acquired from roadside-installed microphone. In this work a visual analytic for consideration of frame size and shift size, while extracting feature vectors using Mel Frequency Cepstral Coefficients (MFCC) for Traffic Density state estimation and corresponding experimental validation is provided. Different kernel functions of support vector machine (SVM) from single acoustic frame to multiple contiguous frames were used to classify the Density state as low, medium and heavy. The system results in enhanced classification performance when observed time increases or when multiple contiguous frames were considered.
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Acoustic signal based Traffic Density state estimation using adaptive Neuro-Fuzzy classifier
2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013Co-Authors: Prashant Borkar, L.g. MalikAbstract:Traffic monitoring and parameters estimation from urban to battlefield environment Traffic is fast-emerging field based on acoustic signals. This paper considers the problem of vehicular Traffic Density state estimation, based on the information present in cumulative acoustic signal acquired from a roadside-installed single microphone. The occurrence and mixture weightings of Traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) are determined by the prevalent Traffic Density conditions on the road segment. In this work, we extract the short-term spectral envelope features of the cumulative acoustic signals using MFCC (Mel-Frequency Cepstral Coefficients). The (Scaled Conjugate Gradient) SCG algorithm, which is a supervised learning algorithm for network-based methods, is used to computes the second-order information from the two first-order gradients of the parameters by using all the training datasets. Adaptive Neuro-Fuzzy classifier is used to model the Traffic Density state as Low (40 Km/h and above), Medium (20-40 Km/h), and Heavy (0-20 Km/h). For the developing geographies where the Traffic is non-lane driven and chaotic, other techniques (magnetic loop detectors) are inapplicable. Adaptive Neuro-Fuzzy classifier is used to classify the acoustic signal segments spanning duration of 20-40 s, which results in a classification accuracy achieved using Adaptive Neuro-Fuzzy classifier is of 93.33% for 13-D MFCC coefficients, Approx 96% when entire feature frames were considered for classification.
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Cumulative Acoustic Signal Based Traffic Density State Estimation
2013 Third International Conference on Advances in Computing and Communications, 2013Co-Authors: Prashant Borkar, L.g. MalikAbstract:Based on the information present in cumulative acoustic signal acquired from a roadside-installed single microphone, this paper considers the problem of vehicular Traffic Density state estimation. The occurrence and mixture weightings of Traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) are determined by the prevalent Traffic Density conditions on the road segment. In this work, we extract the short-term spectral envelope features of the cumulative acoustic signals using LPC (Linear Predictive Coding). Support Vector Machines (SVM) is used as classifier is used to model the Traffic Density state as Low (40 Km/h and above), Medium (20-40 Km/h), and Heavy (0-20 Km/h). For the developing geographies where the Traffic is non-lane driven and chaotic, other techniques (magnetic loop detectors) are inapplicable. SVM classifier with different kernels are used to classify the acoustic signal segments spanning duration of 20-40 s, which results in average classification accuracy of 98.33% and 96.67% for quadratic and polynomial kernel functions respectively.
Marco Fiore - One of the best experts on this subject based on the ideXlab platform.
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VTC Spring - MobSampling: V2V Communications for Traffic Density Estimation
2011 IEEE 73rd Vehicular Technology Conference (VTC Spring), 2011Co-Authors: Laura Garelli, Carla-fabiana Chiasserini, Claudio Casetti, Marco FioreAbstract:We propose a fully-distributed approach to the on-line estimation of vehicle Traffic Density. Our approach envisions vehicles communicating within a VANET and cooperating to collect Density measurements through a uniform sampling of the road sections of interest. The proposed scheme does not require the presence of any network infrastructure, central controller or devices triggered by the passage of vehicles, and it is suitable for both highway and urban environments. Results derived through ns-2 simulations in realistic mobility scenarios show that our solution is very effective, providing accurate, on-line estimates of the Traffic Density with minimal protocol overhead.
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MobSampling: V2V communications for Traffic Density estimation
IEEE Vehicular Technology Conference, 2011Co-Authors: Laura Garelli, Carla-fabiana Chiasserini, Claudio Casetti, Marco FioreAbstract:We propose a fully-distributed approach to the on line estimation of vehicle Traffic Density. Our approach envisions vehicles communicating within a VANET and cooperating to collect Density measurements through a uniform sampling of the road sections of interest. The proposed scheme does not require the presence of any network infrastructure, central controller or devices triggered by the passage of vehicles, and it is suitable for both highway and urban environments. Results derived through ns-2 simulations in realistic mobility scenarios show that our solution is very effective, providing accurate, on-line estimates of the Traffic Density with minimal protocol overhead.
Prashant Borkar - One of the best experts on this subject based on the ideXlab platform.
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Acoustic Signature-based Vehicular Traffic Density State Estimation in Developing Regions
International Journal of Vehicle Noise and Vibration, 2016Co-Authors: Prashant Borkar, Milindkumar V. Sarode, L.g. MalikAbstract:In developing regions like Asia, where the Traffic conditions are chaotic and non-lane driven, the intrusive techniques may be inapplicable. The vehicular acoustic signals and the occurrence and mixture weighting of these signals are determined by the prevalent Traffic Density state condition. This research work considers the problem of vehicular Traffic Density state estimation, based on the information present in the acoustic signal acquired from roadside-installed microphone. In this work a visual analytic for consideration of frame size and shift size, while extracting feature vectors using Mel Frequency Cepstral Coefficients (MFCC) for Traffic Density state estimation and corresponding experimental validation is provided. Different kernel functions of support vector machine (SVM) from single acoustic frame to multiple contiguous frames were used to classify the Density state as low, medium and heavy. The system results in enhanced classification performance when observed time increases or when multiple contiguous frames were considered.
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Acoustic signal based Traffic Density state estimation using adaptive Neuro-Fuzzy classifier
2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013Co-Authors: Prashant Borkar, L.g. MalikAbstract:Traffic monitoring and parameters estimation from urban to battlefield environment Traffic is fast-emerging field based on acoustic signals. This paper considers the problem of vehicular Traffic Density state estimation, based on the information present in cumulative acoustic signal acquired from a roadside-installed single microphone. The occurrence and mixture weightings of Traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) are determined by the prevalent Traffic Density conditions on the road segment. In this work, we extract the short-term spectral envelope features of the cumulative acoustic signals using MFCC (Mel-Frequency Cepstral Coefficients). The (Scaled Conjugate Gradient) SCG algorithm, which is a supervised learning algorithm for network-based methods, is used to computes the second-order information from the two first-order gradients of the parameters by using all the training datasets. Adaptive Neuro-Fuzzy classifier is used to model the Traffic Density state as Low (40 Km/h and above), Medium (20-40 Km/h), and Heavy (0-20 Km/h). For the developing geographies where the Traffic is non-lane driven and chaotic, other techniques (magnetic loop detectors) are inapplicable. Adaptive Neuro-Fuzzy classifier is used to classify the acoustic signal segments spanning duration of 20-40 s, which results in a classification accuracy achieved using Adaptive Neuro-Fuzzy classifier is of 93.33% for 13-D MFCC coefficients, Approx 96% when entire feature frames were considered for classification.
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Acoustic Signal based Traffic Density State Estimation using SVM
International Journal of Image Graphics and Signal Processing, 2013Co-Authors: Prashant Borkar, Latesh MalikAbstract:Based on the information present in cumulative acoustic signal acquired from a roadside- installed single microphone, this paper considers the problem of vehicular Traffic Density state estimation. The occurrence and mixture weightings of Traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) are determined by the prevalent Traffic Density conditions on the road segment. In this work, we extract the short-term spectral envelope features of the cumulative acoustic signals using MFCC (Mel- Frequency Cepstral Coefficients). Support Vector Machines (SVM) is used as classifier is used to model the Traffic Density state as Low (40 Km/h and above), Medium (20-40 Km/h), and Heavy (0-20 Km/h). For the developing geographies where the Traffic is non-lane driven and chaotic, other techniques (magnetic loop detectors) are inapplicable. SVM classifier with different kernels are used to classify the acoustic signal segments spanning duration of 20-40 s, which results in average classification accuracy of 96.67% for Quadratic kernel function and 98.33% for polynomial kernel function, when entire frames are considered for classification.
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Cumulative Acoustic Signal Based Traffic Density State Estimation
2013 Third International Conference on Advances in Computing and Communications, 2013Co-Authors: Prashant Borkar, L.g. MalikAbstract:Based on the information present in cumulative acoustic signal acquired from a roadside-installed single microphone, this paper considers the problem of vehicular Traffic Density state estimation. The occurrence and mixture weightings of Traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) are determined by the prevalent Traffic Density conditions on the road segment. In this work, we extract the short-term spectral envelope features of the cumulative acoustic signals using LPC (Linear Predictive Coding). Support Vector Machines (SVM) is used as classifier is used to model the Traffic Density state as Low (40 Km/h and above), Medium (20-40 Km/h), and Heavy (0-20 Km/h). For the developing geographies where the Traffic is non-lane driven and chaotic, other techniques (magnetic loop detectors) are inapplicable. SVM classifier with different kernels are used to classify the acoustic signal segments spanning duration of 20-40 s, which results in average classification accuracy of 98.33% and 96.67% for quadratic and polynomial kernel functions respectively.
José M. F. Moura - One of the best experts on this subject based on the ideXlab platform.
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Understanding Traffic Density from Large-Scale Web Camera Data
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017Co-Authors: Shanghang Zhang, Guanhang Wu, João P. Costeira, José M. F. MouraAbstract: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 optimization based and deep learning based methods. To avoid individual vehicle detection or tracking, both methods map the dense image feature into vehicle Density, one based on rank constrained regression and the other based on fully convolutional networks (FCN). The regression based method learns different weights for different blocks of the image to embed road geometry and significantly reduce the error induced by camera perspective. The FCN based method jointly estimates vehicle Density 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 (MAE) from 10.99 to 5.31 on the public dataset TRANCOS compared with the state-of-the-art baseline.
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understanding Traffic Density from large scale web camera data
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Shanghang Zhang, Guanhang Wu, João P. Costeira, José M. F. MouraAbstract: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.
