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Study on Target Tracking Based on Vision and Radar Sensor Fusion
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 03, 2018 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Faced with intricate traffic conditions, the single sensor has been unable to meet the safety requirements of Advanced Driver Assistance Systems (ADAS) and autonomous driving. In the field of multi-target tracking, the number of targets detected by vision sensor is sometimes less than the current tracks while the number of targets detected by millimeter wave radar is more than the current tracks. Hence, a multi-sensor information fusion algorithm is presented by utilizing advantage of both vision sensor and millimeter wave radar. The multi-sensor fusion algorithm is based on centralized fusion strategy that the fusion center takes a unified track management. At First, vision sensor and radar are used to detect the target and to measure the range and the azimuth angle of the target. Then, the detections data from vision sensor and radar is transferred to fusion center to join the multi-target tracking with the prediction of current tracks. Vision sensor uses Global Nearest Neighbor (GNN), and radar uses Probabilistic Data Association (PDA) for data association. For target detection, the vision sensor has high accuracy at azimuth angle and low accuracy at range, while radar has medium accuracy at azimuth angle and very high accuracy at range. The detection properties of two sensors should be considered when designing the association gate. Simulation based on real test data which was taken by a monocular camera and a 77GHz millimeter wave radar is performed in MATLAB. Simulation result indicates that the design of association gate has a great impact on fusion performance.
CitationWu, X., Ren, J., Wu, Y., and Shao, J., "Study on Target Tracking Based on Vision and Radar Sensor Fusion," SAE Technical Paper 2018-01-0613, 2018, https://doi.org/10.4271/2018-01-0613.
Data Sets - Support Documents
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