Traffic modeling and prediction using sensor networks
Abstract
We propose a probabilistic 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 human detection and matching of their images taken from cameras at different locations. Experiments with more than 100,000 images of over 40 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 01, 2012
- Source ID
- 10.1145/2379799.2379805
Entities
People
- Ming-Hsuan Yang
- Sangseok Yoon
- Songhwai Oh
- Zaihong Shuai
Organizations
- Army Research Office
- Ministry of Education, Science and Technology
- Seoul National University
- University of California