Traffic modeling and prediction using camera sensor networks

Zaihong Shuai, Songhwai Oh, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

We propose a Bayesian framework for modeling and predicting traffic patterns using information obtained from wireless sensor networks. For concreteness, we apply the proposed framework to a smart building application in which traffic patterns of humans are modeled and predicted through detection and matching of their images taken from cameras at different locations. Experiments with more than 2,500 images of 20 subjects demonstrate promising results in traffic pattern prediction using the proposed algorithm. The algorithm can also be applied to other applications including surveillance, traffic monitoring, abnormality detection, and location-based services. In addition, the long-term deployment of the network can be used for security, energy conservation and utilization improvement of smart buildings.

Original languageEnglish
Title of host publicationICDSC - 4th ACM/IEEE International Conference on Distributed Smart Cameras
Pages49-56
Number of pages8
DOIs
Publication statusPublished - 2010 Dec 6
Event4th ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2010 - Atlanta, GA, United States
Duration: 2010 Aug 312010 Sep 4

Publication series

NameICDSC - 4th ACM/IEEE International Conference on Distributed Smart Cameras

Conference

Conference4th ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2010
CountryUnited States
CityAtlanta, GA
Period10/8/3110/9/4

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All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

Shuai, Z., Oh, S., & Yang, M. H. (2010). Traffic modeling and prediction using camera sensor networks. In ICDSC - 4th ACM/IEEE International Conference on Distributed Smart Cameras (pp. 49-56). (ICDSC - 4th ACM/IEEE International Conference on Distributed Smart Cameras). https://doi.org/10.1145/1865987.1865996