10.1.1.60.6641 - MITSUBISHI ELECTRIC RESEARCH LABORATORIES...

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MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Traffic Congestion Estimation Using HMM Models Without Vehicle Tracking Porikli, F.; Li, X. TR2004-019 December 2004 Abstract We propose an unsupervised, low-latency traffic congestion estimation algorithm that operates on the MPEG video data. We extract congestion features directly in the compressed domain, and employ Gaussian Mixture Hidden Markov Models (GM-HMM) to detect traffic condition. First, we construct a multi-dimensional feature vector from the parsed DCT coefficients and motion vectors. Then, we train a set of left-to-right HMM chains corresponding to five traffic patterns (empty, open flow, mild congestion, heavy congestion, and stopped), and use a Maxi- mum Likelihood (ML) criterion to determine the state from the outputs of the separate HMM chains. We calculate a confidence score to assess the reliability of the detection results. The pro- posed method is computationally efficient and modular. Our tests prove that the feature vector is invariant to different illumination conditions, e.g. sunny, cloudy, dark. Furthermore, we do not need to impose different models for different camera setups, thus we significantly reduce the system initialization workload and improve its adaptability. Experimental results show that the precision rate of the presented algorithm is very high around 95%. SPIE This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c ± Mitsubishi Electric Research Laboratories, Inc., 2004 201 Broadway, Cambridge, Massachusetts 02139
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Traffic Congestion Estimation Using HMM Models Without Vehicle Tracking Fatih Porikli and Xiaokun Li Abstract — We propose an unsupervised, low-latency traffic congestion estimation algorithm that operates on the MPEG video data. We extract congestion features directly in the compressed domain, and employ Gaussian Mixture Hidden Markov Models (GM-HMM) to detect traffic condition. First, we construct a multi-dimensional feature vector from the parsed DCT coefficients and motion vectors. Then, we train a set of left-to-right HMM chains corresponding to five traffic patterns (empty, open flow, mild congestion, heavy congestion, and stopped), and use a Maximum Likelihood (ML) criterion to determine the state from the outputs of the separate HMM chains. We calculate a confidence score to assess the reliability of the detection results. The proposed method is computationally efficient and modular. Our tests prove that the
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10.1.1.60.6641 - MITSUBISHI ELECTRIC RESEARCH LABORATORIES...

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