Fade: a vehicle detection and tracking system featuring monocular color vision and radar data fusion

Steux, Bruno; Laurgeau, Claude; Salesse, L.; Wautier, Didier

doi:10.1109/ivs.2002.1188022

Cited by 77 publications

(48 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Object tracking with the use of radar and vision processing systems has been researched [1]- [5]. Radar and vision have the complementary characteristics in directional measurement's accuracy : the vision system provides accurate lateral information, whilst the radar system gives accurate range information of objects.…”

Section: 서 론mentioning

confidence: 99%

Asynchronous Sensor Fusion using Multi-rate Kalman Filter

Son

Kim

Lee

et al. 2014

The Transactions of The Korean Institute of Electrical Engineer

View full text Add to dashboard Cite

-We propose a multi-rate sensor fusion of vision and radar using Kalman filter to solve problems of asynchronized and multi-rate sampling periods in object vehicle tracking. A model based prediction of object vehicles is performed with a decentralized multi-rate Kalman filter for each sensor (vision and radar sensors.) To obtain the improvement in the performance of position prediction, different weighting is applied to each sensor's predicted object position from the multi-rate Kalman filter. The proposed method can provide estimated position of the object vehicles at every sampling time of ECU. The Mahalanobis distance is used to make correspondence among the measured and predicted objects. Through the experimental results, we validate that the post-processed fusion data give us improved tracking performance. The proposed method obtained two times improvement in the object tracking performance compared to single sensor method (camera or radar sensor) in the view point of roots mean square error.

show abstract

Section: 서 론mentioning

confidence: 99%

Asynchronous Sensor Fusion using Multi-rate Kalman Filter

Son

Kim

Lee

et al. 2014

The Transactions of The Korean Institute of Electrical Engineer

View full text Add to dashboard Cite

show abstract

“…Several studies have been reported on their applications including Hofmann et al (2001), Steux et al (2002), Ramzi (2003) and Sole et al (2004). Sensor fusion for vehicle detection can be achieved at different levels to meet different requirements of ADAS applications, for example, combining several sources of raw data to produce new raw data that combines various features such as edges, corners, lines, texture into a feature map (Hofmann et al, 2001;Sole et al, 2004).…”

Section: Autonomous Systemsmentioning

confidence: 99%

Advanced Driver Assistance Systems from Autonomous to Cooperative Approach

2008

View full text Add to dashboard Cite

“…These methods are dedicated to detect either vehicles according their vertical edges, shadows, symmetry, lights [2] or pedestrians and crowd according to human shape and behaviour constraints. Some original methods track sets of features points ( [3]) or edges ( [4]) on obstacles because their relative motion is different from the camera motion.…”

Section: A Related Workmentioning

confidence: 99%

Obstacle detection from IPM and super-homography

Simond

Parent

2007

2007 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Abstract-We present in this article a simple method to estimate an IPM view from an embedded camera. The method is based on the tracking of the road markers assuming that the road is locally planar. Our aim is the development of a freespace estimator which can be implemented in an Autonomous Guided Vehicle to allow a safe path planning. Opposite to most of the obstacle detection methods which make assumptions on the shape or height of the obstacles, all the scene elements above the road plane (particularly kerbs and poles) have to be detected as obstacles. Combined with the IPM tranformation, this obstacle detection stage can be viewed as the first stage of a free-space estimator dedicated to AGV in the complex urban environments.

show abstract

Fade: a vehicle detection and tracking system featuring monocular color vision and radar data fusion

Cited by 77 publications

References 3 publications

Asynchronous Sensor Fusion using Multi-rate Kalman Filter

Asynchronous Sensor Fusion using Multi-rate Kalman Filter

Advanced Driver Assistance Systems from Autonomous to Cooperative Approach

Obstacle detection from IPM and super-homography

Contact Info

Product

Resources

About