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.
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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)
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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
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DOI: https://doi.org/10.1007/978-3-642-04697-1_45
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