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Object Tracking Comparison for Automated Vehicles Using MathWorks Toolsets
Technical Paper
2021-01-0110
ISSN: 0148-7191, e-ISSN: 2688-3627
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SAE WCX Digital Summit
Language:
English
Abstract
Object trackers are a tool to achieve accurate object state estimation over time. Due to their complexity, a framework to experiment with different variations of trackers and their subcomponents is desired. This drove the authors research and experimentation with object tracking using MathWorks toolsets. In this paper, three object trackers - Point Target Tracker (PTT), Gamma Gaussian Inverse Wishart Probability Hypothesis Density (GGIW-PHD), and Gaussian Mixture Probability Hypothesis Density (GM-PHD) - are compared in simulation for track statistics and object/track accuracy. The results show that a rectangular GM-PHD multi object tracker outperforms the other trackers. A follow up is shown using real-world data and the process used to get the sensor data into the appropriate MathWorks format. The impact of COVID-19 prevented the collection of ground truth data so the real-world data cannot be compared using the same metrics. For this reason, the simulation portion of this paper will act as the detailed discussion of fusion and tracking while the real-world testing portion is an overview of the authors’ process of converting real-world sensor data into a format compatible with MathWorks object tracking tools.
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Citation
Bassett, A., Cicotte, D., and Currier, P., "Object Tracking Comparison for Automated Vehicles Using MathWorks Toolsets," SAE Technical Paper 2021-01-0110, 2021, https://doi.org/10.4271/2021-01-0110.Data Sets - Support Documents
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References
- Kivelevitch , E.H. Sensor Fusion Tools in Support of Autonomous Systems AIAA Scitech 2019 Forum San Diego, California Jan 2019
- Crouse , D.F. The Tracker Component Library: Free Routines for Rapid Prototyping IEEE Aerospace and Electronic Systems Magazine 32 5 18 27 May 2017
- Blackman , S. and Popoli , R. 1999
- Vo , B.-N. et al. Multitarget Tracking Wiley Encyclopedia of Electrical and Electronics Engineering Hoboken, NJ Wiley 2015
- Granstrom , K. , Svensson , L. , Reuter , S. , Xia , Y. , and Fatemi , M. Likelihood-Based Data Association for Extended Object Tracking Using Sampling Methods IEEE Transactions on Intelligent Vehicles 3 30 45 2018
- Granstr¨om , K. , Baum , M. , and Reuter , S. Extended Object Tracking: Introduction, Overview and Applications J. Adv. Inf. Fusion 12 2 139 174 2017
- MathWorks https://www.mathworks.com/help/driving/ug/extended-object-tracking.html
- Liang , Z. , Liu , F. , and Gao , J. Improved GGIW-PHD Filter for Maneuvering Non-Ellipsoidal Extended Targets or Group Targets Tracking Based on Sub-Random Matrices PLoS One 2018
- Zhang , H. , Yang , J. , Ge , H. , and Yang , L. An Improved GM-PHD Tracker with Track Management for Multiple Target Tracking 2015 International Conference on Control, Automation and Information Sciences (ICCAIS) 2015
- Beard , M. , Vo , B. , and Vo , B. OSPA(2): Using the OSPA Metric to Evaluate Multi-Target Tracking Performance 2017 International Conference on Control, Automation and Information Sciences (ICCAIS) 2017
- Schuhmacher , D. , Vo , B.-T. , and Vo , B.-N. A Consistent Metric for Performance Evaluation of Multi-Object Filters IEEE Transactions on Signal Processing 56 8 3447 3457 2008
- Matzka , S. and Altendorfer , R. A Comparison of Track-to-Track Fusion Algorithms for Automotive Sensor Fusion 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems Seoul 2008
- Julier , S.J. and Uhlmann , J.K. A Non-Divergent Estimation Algorithm in the Presence of Unknown Correlations Proceedings of the 1997 American Control Conference (Cat. No.97CH36041) 1997