Radar and vision based data fusion - Advanced filtering techniques for a multi object vehicle tracking system

Richter, Eric; Schubert, Robin; Wanielik, Gerd

doi:10.1109/ivs.2008.4621245

Cited by 40 publications

(20 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The combination of the two can ameliorate the weakness of each sensor [160], [161]. In [162] and [163], information fusion between radar and vision sensors was used to probabilistically estimate the positions of vehicles and to propagate estimation uncertainty into decision making, for lane change recommendations on the highway. In [164], vision and radar were combined to detect overtaking vehicles on the highway, using optical flow to detect vehicles entering the camera's field of view.…”

Section: E Fusing Vision With Other Modalitiesmentioning

confidence: 99%

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

811

322

View full text Add to dashboard Cite

Abstract-This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.

show abstract

Section: E Fusing Vision With Other Modalitiesmentioning

confidence: 99%

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

811

322

View full text Add to dashboard Cite

show abstract

“…Richter et al [51] developed an environmental sensing system that detects both stationary and moving objects, using ego-motion data along with information from a monocular camera and a radar. The detection process is again executed individually for the subsystems.…”

Section: Fusing Independent Observationsmentioning

confidence: 99%

Data fusion of radar and stereo vision for detection and tracking of moving objects

Botha

Daalen

Treurnicht

2016

2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech)

View full text Add to dashboard Cite

“…Richter et al present a tracking algorithm through combination of asynchronized observation data using a movement model [4]. They integrated the longitudinal and lateral velocity measurements to obtain object positions at the required instance.…”

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

Radar and vision based data fusion - Advanced filtering techniques for a multi object vehicle tracking system

Cited by 40 publications

References 6 publications

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Data fusion of radar and stereo vision for detection and tracking of moving objects

Asynchronous Sensor Fusion using Multi-rate Kalman Filter

Contact Info

Product

Resources

About