Feature-based level of service classification for traffic surveillance

Pletzer, Felix; Tusch, Roland; Böszörményi, László; Rinner, Bernhard; Sidla, Oliver; Harrer, Manfred; Mariacher, Thomas

doi:10.1109/itsc.2011.6083101

Cited by 6 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The algorithm [1] is designed to run on a smart camera equipped with an Intel Atom 1.6 GHz processor, 1GB RAM and a 1280x1024 color CCD sensor. For evaluation of the algorithm, we used an 11 hours test video with a resolution of 352x288 containing all LOS levels.…”

Section: Video-based Los Detection Methodsmentioning

confidence: 99%

AVSS 2011 demo session: Level of service classification for smart cameras

Pletzer

Rinner

Tusch

et al. 2011

2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

Self Cite

View full text Add to dashboard Cite

At the Industrial Surveillance Day, ASFINAG and the Alpen Adria Universitt Klagenfurt (in particular the Institute of Information Technology and the Institute of Networked and Embedded Systems) demonstrate a show case of their video-based level of service (LOS) classification for smart cameras. This LOS classification system has been developed in a joint Lakeside Labs project in Klagenfurt, Austria. It is part of a case study which aims at improving the quality of traffic messages for the two particular traffic situations level-of-service (LOS) and weather-related road conditions (WRRC) on two dedicated test tracks on Austrian motorways. Using a live connection to a smart camera at one of these test tracks, we plan to show a live demonstration for visual speed estimation and LOS classification. This demo is coordinated with our partner SLR Engineering, which provided the smart cameras for the case study.

show abstract

Section: Video-based Los Detection Methodsmentioning

confidence: 99%

AVSS 2011 demo session: Level of service classification for smart cameras

Pletzer

Rinner

Tusch

et al. 2011

2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

Self Cite

View full text Add to dashboard Cite

show abstract

“…A grouping module groups the features in order to segment the individual vehicles. In [9], the authors describe a method for classifying the traffic state based on optical flow-based motion features and edge-based density features. The proposed method uses a Gaussian radial basis function network for traffic state classification and is also designed to run on smart cameras in real-time.…”

Section: Background and Related Workmentioning

confidence: 99%

Robust Traffic State Estimation on Smart Cameras

Pletzer

Tusch

Böszörményi

et al. 2012

2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance

Self Cite

View full text Add to dashboard Cite

This paper presents a novel method for video-based traffic state detection on motorways performed on smart cameras. Camera calibration parameters are obtained from the known length of lane markings. Mean traffic speed is estimated from Kanade-Lucas-Tomasi (KLT) optical flow method using a robust outlier detection. Traffic density is estimated using a robust statistical counting method. Our method has been implemented on an embedded smart camera and evaluated under different road and illuminationconditions. It achieves a detection rate of more than 95% for stationary traffic.

show abstract

Distributed smart cameras for hard real-time obstacle detection in control applications

Guggi

Rinner

2011

2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras

View full text Add to dashboard Cite

Feature-based level of service classification for traffic surveillance

Cited by 6 publications

References 14 publications

AVSS 2011 demo session: Level of service classification for smart cameras

AVSS 2011 demo session: Level of service classification for smart cameras

Robust Traffic State Estimation on Smart Cameras

Distributed smart cameras for hard real-time obstacle detection in control applications

Contact Info

Product

Resources

About