Multimodal highway monitoring for robust incident detection

Pucher, Michael; Schabus, Dietmar; Schallauer, Peter; Lypetskyy, Yuriy; Graf, Franz; Rainer, Harald; Stadtschnitzer, Michael; Sternig, Sabine; Birchbauer, Josef Alois; Schneider, Wolfgang; Schalko, Bernhard

doi:10.1109/itsc.2010.5625035

Cited by 20 publications

(10 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, challenging weather or illumination conditions degrade the performance significantly. One way of addressing this problem is to fuse the visual data with another type of sensor as proposed in Pucher et al (2010) where a combination of a camera and a microphone array was used. While these approaches are viable and used in practice, they are not well suited for large scale deployment (for example at every freeway ramp) due to their requirements.…”

Section: Paper B Classification Of Driving Direction Using Magnetometersmentioning

confidence: 99%

Modeling of Magnetic Fields and Extended Objects for Localization Applications

Wahlström¹

2015

View full text Add to dashboard Cite

Cover illustration: The cover shows a magnetic dipole field (3.6), which is used as the sensor model in Paper A. The dipole is placed at the height of the front cover with position r 0 = [104 mm, 90 mm, 0 mm] relative to the bottom left corner of the front cover, with the magnetic dipole moment m = [0. 85, 0.53, 0] being orthogonal to its surface. A 3D cutout of the scalar potential for the magnetic dipole ϕ(r) = (r·m) r 3 is displayed on front, back and side cover, where r is the displacement relative to the position of the magnetic dipole. On the front cover, the field lines of the magnetic dipole field are overlaid.Linköping studies in science and technology. Dissertations.No. 1723 Modeling of Magnetic Fields and Extended Objects for Localization Applications AbstractThe level of automation in our society is ever increasing. Technologies like selfdriving cars, virtual reality, and fully autonomous robots, which all were unimaginable a few decades ago, are realizable today, and will become standard consumer products in the future. These technologies depend upon autonomous localization and situation awareness where careful processing of sensory data is required. To increase efficiency, robustness and reliability, appropriate models for these data are needed. In this thesis, such models are analyzed within three different application areas, namely (1) magnetic localization, (2) extended target tracking, and (3) autonomous learning from raw pixel information. Magnetic localization is based on one or more magnetometers measuring the induced magnetic field from magnetic objects. In this thesis we present a model for determining the position and the orientation of small magnets with an accuracy of a few millimeters. This enables three-dimensional interaction with computer programs that cannot be handled with other localization techniques.Further, an additional model is proposed for detecting wrong-way drivers on highways based on sensor data from magnetometers deployed in the vicinity of traffic lanes. Models for mapping complex magnetic environments are also analyzed. Such magnetic maps can be used for indoor localization where other systems, such as gps, do not work.In the second application area, models for tracking objects from laser range sensor data are analyzed. The target shape is modeled with a Gaussian process and is estimated jointly with target position and orientation. The resulting algorithm is capable of tracking various objects with different shapes within the same surveillance region.In the third application area, autonomous learning based on high-dimensional sensor data is considered. In this thesis, we consider one instance of this challenge, the so-called pixels to torques problem, where an agent must learn a closedloop control policy from pixel information only. To solve this problem, highdimensional time series are described using a low-dimensional dynamical model. Techniques from machine learning together with standard tools from control theory are used to autonomously design a controller f...

show abstract

Section: Paper B Classification Of Driving Direction Using Magnetometersmentioning

confidence: 99%

Modeling of Magnetic Fields and Extended Objects for Localization Applications

Wahlström¹

2015

View full text Add to dashboard Cite

show abstract

“…Lane alert and assistance [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] Pedestrian detection Driver monitoring [38][39][40][41][42][43][44][45][46][47] Adaptive environmental sensing and control Efficiency Traffic flow Incident management 101,[113][114][115][116][117][118][119][120][121][122][123][124][125] Video-based tolling [126][127][128][129][130][131][132][133][134][135][136][137]…”

Section: Safetymentioning

confidence: 99%

Computer vision in roadway transportation systems: a survey

Loce

Bernal

et al. 2013

J. Electron. Imaging

View full text Add to dashboard Cite

Abstract. There is a worldwide effort to apply 21st century intelli-gence to evolving our transportation networks. The goals of smart transportation networks are quite noble and manifold, including safety, efficiency, law enforcement, energy conservation, and emis-sion reduction. Computer vision is playing a key role in this transpor-tation evolution. Video imaging scientists are providing intelligent sensing and processing technologies for a wide variety of applications and services. There are many interesting technical challenges includ-ing imaging under a variety of environmental and illumination condi-tions, data overload, recognition and tracking of objects at high speed, distributed network sensing and processing, energy sources, as well as legal concerns. This paper presents a survey of computer vision techniques related to three key problems in the transportation domain: safety, efficiency, and security and law enforcement. A broad review of the literature is complemented by detailed treatment of a few selected algorithms and systems that the authors believe represent the state-of-the-art. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JEI.22.4.041121]

show abstract

“…In [11,10], the authors employ classifier grids with adaptive online learning for detecting cars. The feature-based KLT algorithm is used to calculate the velocity of vehicles.…”

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

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

Multimodal highway monitoring for robust incident detection

Cited by 20 publications

References 13 publications

Modeling of Magnetic Fields and Extended Objects for Localization Applications

Modeling of Magnetic Fields and Extended Objects for Localization Applications

Computer vision in roadway transportation systems: a survey

Robust Traffic State Estimation on Smart Cameras

Contact Info

Product

Resources

About