Abstract
This paper presents the tracking system from Athens Information Technology that participated to the pedestrian and vehicle surveillance task of the CLEAR 2007 evaluations and the obtained results. The system is based on the CLEAR 2006 one, with some important modifications that are detailed. Since the test data in CLEAR 2006 and 2007 are the same, it is easy to quantify the obtained performance gain from the older system to the proposed one.
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References
Waibe1, A., Steusloff, H., Stiefelhagen, R., et al.: CHIL: Computers in the Human Interaction Loop. In: 5th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), Lisbon, Portugal (April 2004)
Pnevmatikakis, A., Talantzis, F., Soldatos, J., Polymenakos, L.: Robust Multimodal Audio-Visual Processing for Advanced Context Awareness in Smart Spaces. In: Maglogiannis, I., Karpouzis, K., Bramer, M. (eds.) Artificial Intelligence Applications and Innovations (AIAI 2006), pp. 290–301. Springer, Heidelberg (2006)
Forsyth, D., Ponce, J.: Computer Vision - A Modern Approach, pp. 489–541. Prentice Hall, Englewood Cliffs (2002)
MacCormick, J.: Probabilistic modelling and stochastic algorithms for visual localisation and tracking, PhD Thesis, University of Oxford, section 4.6 (2000)
Jaffré, G., Crouzil, A.: Non-rigid object localization from color model using mean shift. In: International Conference on Image Processing (ICIP 2003), Barcelona, Spain (September 2003)
Ekenel, H., Pnevmatikakis, A.: Video-Based Face Recognition Evaluation in the CHIL Project – Run 1. In: Face and Gesture Recognition, Southampton, UK, pp. 85–90 (March 2006)
McIvor, A.: Background Subtraction Techniques, Image and Vision Computing, New Zealand (2000)
Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. on Pattern Anal. and Machine Intel. 22(8), 747–757 (2000)
KaewTraKulPong, P., Bowden, R.: An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection. In: Proc. 2nd European Workshop on Advanced Video Based Surveillance Systems (AVBS 2001), (September 2001)
Landabaso, J.L., Pardas, M.: Foreground regions extraction and characterization towards real-time object tracking. In: Renals, S., Bengio, S. (eds.) MLMI 2005. LNCS, vol. 3869, Springer, Heidelberg (2006)
Kalman, R.E.: A New Approach to Linear Filtering and Prediction Problems. Transactions of the ASME – Journal of Basic Engineering 82 (Series D), 35–45 (1960)
Xu, L.-Q., Landabaso, J.L., Pardas, M.: Shadow Removal with Blob-Based Morphological Reconstruction for Error Correction. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (March 2005)
Blackman, S.: Multiple-Target Tracking with Radar Applications, ch. 14. Artech House, Dedham (1986)
Zhang, Z.: A Flexible New Technique for Camera Calibration, Microsoft Research, Technical Report MSR-TR-98-71 (August 2002)
Jones, M., Rehg, J.: Statistical color models with application to skin detection. Computer Vision and Pattern Recognition, 274–280 (1999)
Viola, P., Jones, M.: Rapid Object Detection using a Boosted Cascade of Simple Features. In: IEEE Conf. on Computer Vision and Pattern Recognition (2001)
Herman, S.-M.: A particle filtering approach to joint passive radar tracking and target classification, PhD thesis, University of Illinois at Urbana-Champaign, 51–54 (2002)
Bloom, H.A.P., Bar-Shalom, Y.: The interactive multiple model algorithm for systems with Markovian switching coefficients. IEEE Trans. Automatic Control 33, 780–783 (1988)
Watson, G.A., Blair, W.D.: IMM algorithm for tracking targets that maneuver through coordinated turns. In: Proc. of SPIE Signal and Data Processing of Small Targets, vol. 1698, pp. 236–247 (1992)
Kasturi, R., et al.: Performance evaluation protocol for face, person and vehicle detection & tracking in video analysis and content extraction (VACE-II), University of South Florida (January 2006)
Isard, M., Blake, A.: CONDENSATION - conditional density propagation for visual tracking. Int. J. Computer Vision 29, 5–28 (1998)
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Stergiou, A., Pnevmatikakis, A., Polymenakos, L. (2008). The AIT Outdoor Tracker for Vehicles and Pedestrians in CLEAR2007. In: Stiefelhagen, R., Bowers, R., Fiscus, J. (eds) Multimodal Technologies for Perception of Humans. RT CLEAR 2007 2007. Lecture Notes in Computer Science, vol 4625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68585-2_12
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DOI: https://doi.org/10.1007/978-3-540-68585-2_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68584-5
Online ISBN: 978-3-540-68585-2
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