Abstract
In this study, we deal with the problem of detecting the trajectories of moving vehicles. We introduce a method, based on the spatio-temporal connectivity analysis, to extract the vehicles trajectories from temporal templates, spanned over a short period of time. Temporal templates are conformed with the successive images differences. The trajectories are computed using the centers of the blobs in the temporal template. A Kalman filter for a constant value with emphasis in the measurement uncertainty is used to smooth the result. The algorithm is tested extensively using a sequence took from tower overlooking a vehicular intersection. Our approach allow us to detect the vehicles trajectories without the need to construct a background model or using a sophisticated tracking strategy for the moving objects. Our experiments show that the scheme we propose is reliable, and fast.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Beymer, D., McLauchlan, P., Coifman, B., Malik, J.: A Real-Time Computer Vision System for Measuring Traffic Parameters. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 495–501 (1997)
Bobick, A., Davis, J.: An Appearance-Based Representation of Action. In: IEEE International Conference on Pattern Recognition, vol. 1 (1996)
Bobick, A., Davis, J.: The Recognition of Human Movement using Temporal Templates. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(3), 257–267 (2001)
Chen, S.C., Shyu, M.L., Peeta, S., Zhang, C.: Spatiotemporal Vehicle Tracking: The Use of Unsupervised Learning-Based Segmentation and Object Tracking. IEEE Robotics & Automation Magazine 12(1), 50–58 (2005)
Foresti, G.L., Murino, V., Regazzoni, C.: Vehicle Recognition and Tracking from Road Image Sequences. IEEE Transactions on Vehicular Technology 48(1), 301–318 (1999)
Gardner, W.F., Lawton, D.T.: Interactive Model-Based Vehicle Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(11), 1115–1121 (1996)
Hsieh, J.W., Yu, S.H., Chen, Y.S., Hu, W.F.: Automatic Traffic Surveillance System for Vehicle Tracking and Classification. IEEE Transactions on Intelligent Transportation Systems 7(2), 175–187 (2006)
Hu, W., Xiao, X., Xie, D., Tan, T., Maybank, S.: Traffic accident prediction using 3-D model-based vehicle tracking. IEEE Transactions on Vehicular Technology 53(3), 677–694 (2004)
Kim, Z., Malik, J.: Fast Vehicle Detection with Probabilistic Feature Grouping and its Application to Vehicle Tracking. In: IEEE International Conference on Computer Vision, vol. 1, pp. 524–531 (2003)
Kumar, S., Kumar, D.K., Sharma, A., McLachlan, N.: Visual Hand Gestures Classification using Temporal Motion Templates. In: International Conference on Multimedia Modelling (2004)
Leibe, B., Schindler, K., Cornelis, N., Van Gool, L.: Coupled object detection and tracking from static cameras and moving vehicles. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(10), 1683–1698 (2008)
Liu, J., Zheng, N.: Gait History Image: A Novel Temporal Template for Gait Recognition. In: IEEE International Conference on Multimedia and Expo., pp. 663–666 (2007)
Lou, J., Tan, T., Hu, W.: Visual Vehicle Tracking Algorithm. Electronics Letters 38(18), 1024–1025 (2002)
Lou, J., Tan, T., Hu, W., Yang, H., Maybank, S.J.: 3-D Model-Based Vehicle Tracking. IEEE Transactions on Image Processing 14(10), 1561 (2005)
Magee, D.R.: Tracking Multiple Vehicles using Foreground, Background and Motion Models. Image and Vision Computing 22(2), 143–155 (2004)
Makris, D., Ellis, T.: Finding Paths in Video Sequences. In: British Machine Vision Conference (2001)
Maurin, B., Masoud, O., Papanikolopoulos, N.: Tracking All Traffic: Computer Vision Algorithms for Monitoring Vehicles, Individuals, and Crowds. IEEE Robotics & Automation Magazine 12(1), 29–36 (2005)
Morris, B., Trivedi, M.: Learning, Modeling, and Classification of Vehicle Track Patterns from Live Video. IEEE Transactions on Intelligent Transportation Systems 9(3), 425–437 (2008)
Piccardi, M.: Background Subtraction Techniques: A Review. In: IEEE (ed.) IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3099–3104 (2004)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Englewood Cliffs (1995)
Shi, J., Tomasi, C.: Good Features to Track. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 1994), Seattle (June 1994)
Stauffer, C., Grimson, W.: Learning Patterns of Activity using Real-Time Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 747–757 (2000)
Valstar, M., Patras, I., Pantic, M.: Facial action unit recognition using temporal templates. In: 13th IEEE International Workshop on Robot and Human Interactive Communication, 2004. ROMAN 2004, pp. 253–258 (2004)
Welch, G., Bishop, G.: An introduction to the Kalman filter. Technical Report TR 95-041, University of North Carolina at Chapel Hill, Department of Computer Science, Chapel Hill, NC 27599-3175 (1995)
Zhong, H., Shi, J., Visontai, M.: Detecting Unusual Activity in Video. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 819–826 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jiménez, H., Salas, J. (2009). Temporal Templates for Detecting the Trajectories of Moving Vehicles. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2009. Lecture Notes in Computer Science, vol 5807. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04697-1_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-04697-1_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04696-4
Online ISBN: 978-3-642-04697-1
eBook Packages: Computer ScienceComputer Science (R0)