A Vehicular Visual Tracking System Incorporating Global Positioning System

Surveillance system is widely used in the traffic monitoring. The deployment of cameras is moving toward a ubiquitous camera (UbiCam) environment. In our previous study, a novel service, called GPS-VT, was firstly proposed by incorporating global positioning system (GPS) and visual tracking techniques for the UbiCam environment. The first prototype is called GODTA (GPS-based Moving Object Detection and Tracking Approach). For a moving person carried GPS-enabled mobile device, he can be tracking when he enters the field-of-view (FOV) of a camera according to his real-time GPS coordinate. In this paper, GPS-VT service is applied to the tracking of vehicles. The moving speed of a vehicle is much faster than a person. It means that the time passing through the FOV is much shorter than that of a person. Besides, the update interval of GPS coordinate is once per second, it is asynchronous with the frame rate of the real-time image. The above asynchronous is worsen by the network transmission delay. These factors are the main challenging to fulfill GPS-VT service on a vehicle.In order to overcome the influence of the above factors, a back-propagation neural network (BPNN) is used to predict the possible lane before the vehicle enters the FOV of a camera. Then, a template matching technique is used for the visual tracking of a target vehicle. The experimental result shows that the target vehicle can be located and tracking successfully. The success location rate of the implemented prototype is higher than that of the previous GODTA.

Authors:

• Hsien-Chou Liao
• Yu-Shiang Wang

Keywords:

• visual surveillance
• visual tracking
• globalpositioning system
• intelligent transportation system

References:

[1] H. C. Liao and P. T. Chu, "A Novel Visual Tracking Approach
Incorporating Global Positioning System in a Ubiquitous Camera
Environment," Information Technology Journal, Vol. 8, No. 4, 2009, pp.
465-475.
[2] H. C. Liao and H. J. Wu, "Automatic Camera Calibration and
Rectification Methods," Measurement + Control Journal, Vol. 43, No. 8,
Oct. 2010, pp. 251-254.
[3] L. Kane, B. Verma, and S. Jain, "Vehicle Tracking in Public Transport
Domain and Associated Spatio-temporal Query Processing," Computer
Communications, Vol. 31, Issue 12, July 2008, pp. 2862-2869 .
[4] G. Derekenaris, J. Garofalakis, C. Makris, J. Prentzas, S. Sioutas, and A.
Tsakalidis, "Integrating GIS, GPS and GSM technologies for the
Effective Management of Ambulances," Computers, Environment and
Urban Systems, Vol. 25, Issue 3, May 2001, pp. 267-278.
[5] W. H. Lee, S. S. Tseng, and C. H. Wang, "Design and Implementation of
Electronic Toll Collection System based on Vehicle Positioning System
Techniques," Computer Communications, Vol. 31, Issue 12, July 2008,
pp. 2925-2933.
[6] N. K. Kanhere and S. T. Birchfield, "Real-Time Incremental
Segmentation and Tracking of Vehicles at Low Camera Angles Using
Stable Features," IEEE Transactions on Intelligent Transportation
Systems, Vol. 9, Issue 1, 2008, pp. 148-160.
[7] J. Zhou, D. Gao, and D. Zhang, "Moving Vehicle Detection for
Automatic Traffic Monitoring," IEEE Transactions on Vehicular
Technology, Vol. 56, Issue 1, 2007, pp. 51-59.
[8] B. T. Morris and M. M. Trivedi, "Learning, Modeling, and
Classification of Vehicle Track Patterns from Live Video", IEEE
Transactions on Intelligent Transportation Systems, Vol. 9, Issue 3,
2008, pp. 425-437.
[9] J. W. Hsieh, S. H. Yu, Y. S. Chen, and W. F. Hu, "Automatic Traffic
Surveillance System for Vehicle Tracking and Classification," IEEE
Transactions on Intelligent Transportation Systems, Vol. 7, Issue 2,
2006, pp. 175-187.
[10]T. Y. Kwok and D. Y. Yeung, "Constructive Algorithms for Structure
Learning in Feedforward Neural Networks for Regression Problems,"
IEEE Transactions on Neural Networks, Vol. 8, Issue 3, 1997, pp.
630-645.
[11] AForge.Net Framework: http://code.google.com/p/aforge