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Proceedings ArticleDOI

Road traffic model using distributed camera network

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TLDR
Unlike the existing methods it does not assume or draws analogies of traffic moving as particles, neither does it impose restriction on road conditions or road tributaries and distributaries, and is flexible, adaptive, robust and computationally light.
Abstract: 
Traffic monitoring/prediction using a distributed camera network is presented in this paper. The activities on each road link are monitored and features are derived to identify the pattern. Then it is learnt, classified, predicted and communicated to neighboring road links. We used GMM-EM based classification and HMM based prediction. Optimum path is determined by assigning proportional weights to the predicted states of the connected road links. The proposed method is neither based on tracking nor on vehicle detection. Apart from this the method is flexible, adaptive, robust and computationally light. Unlike the existing methods it does not assume or draws analogies of traffic moving as particles, neither does it impose restriction on road conditions or road tributaries and distributaries. The model is validated using traffic simulator and tested on real road network.

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Citations
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Video based adaptive road traffic signaling

TL;DR: A video based adaptive traffic signaling scheme for reducing waiting period of vehicles at road junctions without detecting or tracking vehicles is proposed and found to be a much faster and effective control strategy.
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3D pedestrian tracking based on overhead cameras

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An automated urban traffic control system for heavy traffic congestion

TL;DR: An automated urban traffic control system for heavy traffic congestion that will provide the authorities of technologically underdeveloped countries with the facility to monitor, control and maintain the traffic signals in a completely automated manner is proposed.
References
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Journal ArticleDOI

A tutorial on hidden Markov models and selected applications in speech recognition

TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Journal ArticleDOI

Scale & Affine Invariant Interest Point Detectors

TL;DR: A comparative evaluation of different detectors is presented and it is shown that the proposed approach for detecting interest points invariant to scale and affine transformations provides better results than existing methods.
Journal Article

A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models

TL;DR: In this paper, the authors describe the EM algorithm for finding the parameters of a mixture of Gaussian densities and a hidden Markov model (HMM) for both discrete and Gaussian mixture observation models.
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