On Tuesday, May 28, scheduled routine maintenance may cause intermittent connectivity issues which could impact e-commerce, registration, and single sign-on. Thank you for your patience.

Technical Papers
Feb 15, 2019

Multiple Hypothesis Tracking with Kinematics and Appearance Models on Traffic Flow for Wide Area Traffic Surveillance

Publication: Journal of Computing in Civil Engineering
Volume 33, Issue 3

Abstract

This paper presents a study of multiple hypothesis tracking (MHT) of vehicles recorded in wide area motion imagery (WAMI) that has persistent coverage. To take advantage of visual information contained in such aerial imagery, the authors propose a novel MHT-KAM method that combines multiple hypothesis tracking (MHT) with a kinematics and appearance model (KAM). Experiments were designed and implemented to test MHT-KAM on synthetic data sets with various frame rates, traffic configurations, and detection error rates. The experimental results indicate that this method can achieve promising performance for tracking individual vehicles, even in saturated traffic flow. The experimental findings indicate that the combination of applying high appearance weights in MHT-KAM and using large Mahalanobis distance-based gating solves the longstanding “closely-spaced targets” problem. The results also reveal satisfactory performance on existing aerial imagery data sets with limited quality and frame rates. This novel MHT-KAM method combined with previous computer vision-based approach has the potential to achieve a reliable and robust traffic surveillance system for extracting accurate microscopic data from persistent WAMI for diverse applications.

Get full access to this article

View all available purchase options and get full access to this article.

References

Al-Shakarji, N. M., F. Bunyak, G. Seetharaman, and K. Palaniappan. 2018. “Robust multi-object tracking with semantic color correlation.” In Proc., SPIE—The Int. Society for Optical Engineering, 1–7. Bellingham, WA: SPIE.
Antunes, D. M., D. M. de Matos, and J. Gaspar. 2011. “A library for implementing the multiple hypothesis tracking algorithm.” Preprint, submitted June 11, 2011. http://arxiv.org/abs/1106.2263v1.
Arambel, P. O., J. Silver, J. Krant, M. Antone, and T. Strat. 2004. “Multiple-hypothesis tracking of multiple ground targets from aerial video with dynamic sensor control.” In Proc., SPIE—The Int. Society for Optical Engineering, 23–32. Bellingham, WA: SPIE.
Blackman, S. S. 2004. “Multiple hypothesis tracking for multiple target tracking.” IEEE Aerosp. Electron. Syst. Mag. 19 (1): 5–18. https://doi.org/10.1109/MAES.2004.1263228.
Blackman, S. S., R. J. Dempster, and R. W. Reed. 2001. “Demonstration of multiple-hypothesis tracking (MHT) practical real-time implementation feasibility.” In Proc., SPIE—The Int. Society for Optical Engineering, 470–475. Bellingham, WA: SPIE.
Cao, X., J. Lan, P. Yan, and X. Li. 2012. “Vehicle detection and tracking in airborne videos by multi-motion layer analysis.” Mach. Vision Appl. 23 (5): 921–935. https://doi.org/10.1007/s00138-011-0336-x.
Chen, B. J., and G. Medioni. 2015a. “3-D mediated detection and tracking in wide area aerial surveillance.” In Proc., IEEE Winter Conf. Appl. on Computer Vision, 396–403. Los Alamitos, CA: IEEE Computer Society.
Chen, B. J., and G. Medioni. 2015b. “Motion propagation detection association for multi-target tracking in wide area aerial surveillance.” In Proc., IEEE Int. Conf. on Advanced Video and Signals-Based Surveillance, 1–6. Piscataway, NJ: IEEE.
Chen, B. J., and G. Medioni. 2017. “Exploring local context for multi-target tracking in wide area aerial surveillance.” In Proc., IEEE Winter Conf. on Applications Computer Vision, 787–796. Los Alamitos, CA: IEEE Computer Society.
Coraluppi, S., D. Grimmett, and P. De Theije. 2006. “Benchmark evaluation of multistatic trackers.” In Proc., Int. Conf. on Information Fusion, 1–7. Los Alamitos, CA: IEEE Computer Society.
Cox, I. J., and S. L. Hingorani. 1996. “An efficient implementation of Reid’s multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking.” IEEE Trans. Pattern Anal. Mach. Intell. 18 (2): 138–150. https://doi.org/10.1109/34.481539.
Donahue, J., J. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. 2014. “DeCAF: A deep convolutional activation feature for generic visual recognition.” In Proc., Int. Conf. on Machine Learning, 988–996. Princeton, NJ: International Machine Learning Society.
FHWA. 2016. Traffic monitoring guide. Washington, DC: FHWA.
Genovese, A. F. 2001. “The interacting multiple model algorithm for accurate state estimation of maneuvering targets.” Johns Hopkins APL Tech. Digest 22 (4): 614–623.
Gorji, A. A., R. Tharmarasa, and T. Kirubarajan. 2011. “Performance measures for multiple target tracking problems.” In Proc., Int. Conf. on Information Fusion, 1–8. Los Alamitos, CA: IEEE Computer Society.
Hinz, S. 2004. “Detection of vehicles and vehicle queues in high resolution aerial images.” In Proc., World Multi-Conf. on Systemics, Cybernetics and Informatics, 405–410. Paris: International Social Science Council.
Kalal, Z., K. Mikolajczyk, and J. Matas. 2012. “Tracking-learning-detection.” IEEE Trans. Pattern Anal. Mach. Intell. 34 (7): 1409–1422. https://doi.org/10.1109/TPAMI.2011.239.
Kanhere, N. K., S. T. Birchfield, W. A. Sarasua, and S. Khoeini. 2010. “Traffic monitoring of motorcycles during special events using video detection.” Transp. Res. Rec. 2160 (1): 69–76. https://doi.org/10.3141/2160-08.
Kim, C., F. Li, A. Ciptadi, and J. M. Rehg. 2015. “Multiple hypothesis tracking revisited.” In Proc., IEEE Int. Conf. on Computer Vision, 4696–4704. Los Alamitos, CA: IEEE Computer Society.
Kim, Z., and J. Malik. 2003. “Fast vehicle detection with probabilistic feature grouping and its application to vehicle tracking.” In Proc., IEEE Int. Conf. on Computer Vision, 524–531. Los Alamitos, CA: IEEE Computer Society.
Koppa, J. R. 2001. “Human factors.” In Traffic flow theory a state-of-the-art report, 25. Washington, DC: Transportation Research Board.
Leal-Taixé, L., A. Milan, K. Schindler, D. Cremers, I. Reid, and S. Roth. 2017. “Tracking the trackers: An analysis of the state of the art in multiple object tracking.” Preprint, submitted April 10, 2017. http://arxiv.org/abs/1704.02781v1.
Luo, W., J. Xing, A. Milan, X. Zhang, W. Liu, X. Zhao, and T.-K. Kim. 2014. “Multiple object tracking: A literature review.” Preprint, submitted May 22, 2017. http://arxiv.org/abs/arXiv:1409.7618v1.
Mahalanobis, P. C. 1936. “On the generalized distance in statistics.” Proc. Natl. Inst. Sci. India 2 (1): 49–55.
Moon, H., R. Chellappa, and A. Rosenfeld. 2002. “Performance analysis of a simple vehicle detection algorithm.” Image Vision Comput. 20 (1): 1–13. https://doi.org/10.1016/S0262-8856(01)00059-2.
Palaniappan, K., et al. 2016. “Moving object detection for vehicle tracking in wide area motion imagery using 4d filtering.” In Proc., IEEE Int. Conf. on Advanced Video and Signal-Based Surveillance, 2830–2835. Piscataway, NJ: IEEE.
Palaniappan, K., F. Bunyak, P. Kumar, I. Ersoy, S. Jaeger, K. Ganguli, A. Haridas, J. Fraser, R. M. Rao, and G. Seetharaman. 2010. “Efficient feature extraction and likelihood fusion for vehicle tracking in low frame rate airborne video.” In Proc., Int. Conf. on Information Fusion, 1–8. Los Alamitos, CA: IEEE Computer Society.
Palaniappan, K., R. M. Rao, and G. Seetharaman. 2011. “Wide-area persistent airborne video: Architecture and challenges.” In Distributed video sensor networks, 349–371. Berlin: Springer.
Pelapur, R., S. Candemir, F. Bunyak, M. Poostchi, G. Seetharaman, and K. Palaniappan. 2012. “Persistent target tracking using likelihood fusion in wide-area and full motion video sequences.” In Proc., Int. Conf. on Information Fusion, 2420–2427. Los Alamitos, CA: IEEE Computer Society.
Poostchi, M., K. Palaniappan, and G. Seetharaman. 2017. “Spatial pyramid context-aware moving vehicle detection and tracking in urban aerial imagery.” In Proc., IEEE Int. Conf. on Advanced Video and Signal Based Surveillance, 1–6. Piscataway, NJ: IEEE.
Prokaj, J., and G. Medioni. 2014. “Persistent tracking for wide area aerial surveillance.” In Proc., IEEE Conf. Computer Vision and Pattern Recognition, 1186–1193. Los Alamitos, CA: IEEE Computer Society.
Razavian, A. S., H. Azizpour, J. Sullivan, and S. Carlsson. 2014. “CNN features off-the-shelf: An astounding baseline for recognition.” In Proc., IEEE Conf. on Computer Vision and Pattern Recognition, 512–519. Los Alamitos, CA: IEEE Computer Society.
Reid, D. B. 1979. “An algorithm for tracking multiple targets.” IEEE Trans. Autom. Control 24 (6): 843–854. https://doi.org/10.1109/TAC.1979.1102177.
Reinartz, P., M. Lachaise, E. Schmeer, T. Krauss, and H. Runge. 2006. “Traffic monitoring with serial images from airborne cameras.” ISPRS J. Photogramm. Remote Sens. 61 (3): 149–158. https://doi.org/10.1016/j.isprsjprs.2006.09.009.
Saleemi, I., and M. Shah. 2013. “Multiframe many-many point correspondence for vehicle tracking in high density wide area aerial videos.” Int. J. Comput. Vision 104 (2): 198–219. https://doi.org/10.1007/s11263-013-0624-1.
Saunier, N., and T. Sayed. 2010. “Automated analysis of road safety with video data.” Transp. Res. Rec. 2019 (1): 57–64. https://doi.org/10.3141/2019-08.
Shi, X., P. Li, H. Ling, W. Hu, and E. Blasch. 2013. “Using maximum consistency context for multiple target association in wide area traffic scenes.” In Proc., IEEE Int. Conf. Acoust. Acoustics, Speech, and Signal Processing, 2188–2192. Piscataway, NJ: IEEE.
Shitrit, H. B., J. Berclaz, F. Fleuret, and P. Fua. 2011. “Tracking multiple people under global appearance constraints.” In Proc., IEEE Int. Conf. on Computer Vision, 137–144. Los Alamitos, CA: IEEE Computer Society.
Spraul, R., C. Hartung, and T. Schuchert. 2017. “Persistent multiple hypothesis tracking for wide area motion imagery.” In Proc., IEEE Int. Conf. on Image Processing, 1142–1142. Piscataway, NJ: IEEE.
Tsai, Y. J., C. R. Wang, and Y. Wu. 2011. “A vision-based approach to study driver behavior in work zone areas.” In Proc., Int. Conf. on Road Safety Simulation, 14–16. Washington, DC: TRB.
Xiao, J., H. Cheng, H. Sawhney, and F. Han. 2010. “Vehicle detection and tracking in wide field-of-view aerial video.” In Proc., IEEE Conf. on Computer Vision Pattern Recognition, 679–684. Los Alamitos, CA: IEEE Computer Society.
Zhang, L., Y. Li, and R. Nevatia. 2008. “Global data association for multi-object tracking using network flows.” In Proc., IEEE Conf. on Computer Vision Pattern Recognition, 1–8. Los Alamitos, CA: IEEE Computer Society.
Zhao, T., and R. Nevatia. 2003. “Car detection in low resolution aerial images.” Image Vision Comput. 21 (8): 693–703. https://doi.org/10.1016/S0262-8856(03)00064-7.
Zhao, X., D. Dawson, W. A. Sarasua, and S. T. Birchfield. 2016. “Automated traffic surveillance system with aerial camera arrays imagery: Macroscopic data collection with vehicle tracking.” J. Comput. Civ. Eng. 31 (3): 04016072. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000646.

Information & Authors

Information

Published In

Go to Journal of Computing in Civil Engineering
Journal of Computing in Civil Engineering
Volume 33Issue 3May 2019

History

Received: Apr 1, 2017
Accepted: Sep 13, 2018
Published online: Feb 15, 2019
Published in print: May 1, 2019
Discussion open until: Jul 15, 2019

Permissions

Request permissions for this article.

Authors

Affiliations

Xi Zhao, Ph.D. [email protected]
School of Automation, Wuhan Univ. of Technology, Wuhan, Hubei 430070, China; Dept. of Civil Engineering, Clemson Univ., Clemson, SC 29634 (corresponding author). Email: [email protected]
Douglas Dawson [email protected]
Ph.D. Candidate, Dept. of Electrical and Computer Engineering, Clemson Univ., Clemson, SC 29634. Email: [email protected]
Wayne A. Sarasua [email protected]
Associate Professor, Dept. of Civil Engineering, Clemson Univ., Clemson, SC 29634. Email: [email protected]
Stanley T. Birchfield [email protected]
Associate Professor, Dept. of Electrical and Computer Engineering, Clemson Univ., Clemson, SC 29634. Email: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share