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Proceedings Paper

Traffic monitoring with distributed smart cameras
Author(s): Oliver Sidla; Marcin Rosner; Michael Ulm; Gert Schwingshackl
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Paper Abstract

The observation and monitoring of traffic with smart visions systems for the purpose of improving traffic safety has a big potential. Today the automated analysis of traffic situations is still in its infancy--the patterns of vehicle motion and pedestrian flow in an urban environment are too complex to be fully captured and interpreted by a vision system. 3In this work we present steps towards a visual monitoring system which is designed to detect potentially dangerous traffic situations around a pedestrian crossing at a street intersection. The camera system is specifically designed to detect incidents in which the interaction of pedestrians and vehicles might develop into safety critical encounters. The proposed system has been field-tested at a real pedestrian crossing in the City of Vienna for the duration of one year. It consists of a cluster of 3 smart cameras, each of which is built from a very compact PC hardware system in a weatherproof housing. Two cameras run vehicle detection and tracking software, one camera runs a pedestrian detection and tracking module based on the HOG dectection principle. All 3 cameras use sparse optical flow computation in a low-resolution video stream in order to estimate the motion path and speed of objects. Geometric calibration of the cameras allows us to estimate the real-world co-ordinates of detected objects and to link the cameras together into one common reference system. This work describes the foundation for all the different object detection modalities (pedestrians, vehicles), and explains the system setup, tis design, and evaluation results which we have achieved so far.

Paper Details

Date Published: 23 January 2012
PDF: 12 pages
Proc. SPIE 8301, Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques, 830103 (23 January 2012); doi: 10.1117/12.908358
Show Author Affiliations
Oliver Sidla, SLR Engineering OG (Austria)
Marcin Rosner, SLR Engineering OG (Austria)
Michael Ulm, Austrian Institute of Technology (Austria)
Gert Schwingshackl, Sensotech GmbH (Austria)

Published in SPIE Proceedings Vol. 8301:
Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques
Juha Röning; David P. Casasent, Editor(s)

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