Abstract
In this paper we present an approach for automatically detecting and tracking humans in very long video sequences. The detection is based on background subtraction using a multi-mode Codeword method. We enhance this method both in terms of representation and in terms of automatically updating the background allowing for handling gradual and rapid changes. Tracking is conducted by building appearance-based models and matching these over time. Tests show promising detection and tracking results in a ten hour video sequence.
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References
Haritaoglu, I., Harwood, D., Davis, L.: W4: Real-Time Surveillance of People and Their Activities. IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (2000)
McKenna, S., Jabri, S., Duric, Z., Wechsler, H.: Tracking Interacting People. In: The fourth International Conference on Automatic Face and Gesture Recognition, Grenoble, France (2000)
Zhao, T., Nevatia, R.: Tracking Multiple Humans in Crowded Environments. In: Computer Vision and Pattern Recognition, Washington DC, USA (2004)
Park, S., Aggarwal, J.: Simultaneous tracking of multiple body parts of interacting persons. Computer Vision and Image Understanding 102 (2006)
Leibe, B., Seemann, E., Schiele, B.: Pedestrian Detection in Crowded Scenes. In: Computer Vision and Pattern Recognition, San Diego, CA, USA (2005)
Viola, P., Jones, M., Snow, D.: Detecting Pedestrians Using Patterns of Motion and Appearance. International Journal of Computer Vision 63 (2005)
Sidenbladh, H.: Detecting Human Motion with Support Vector Machines. In: International Conference on Pattern Recognition, Cambridge (2004)
Hayashi, K., Hashimoto, M., Sumi, K., Sasakawa, K.: Multiple-Person Tracker with a Fixed Slanting Stereo Camera. In: International Conference on Automatic Face and Gesture Recognition, Seoul, Korea (2004)
Stauffer, C., Grimson, W.: Adaptive Background Mixture Models for Real-Time Tracking. In: Computer Vision and Pattern Recognition, Santa Barbara, CA, USA (1998)
Roth, D., Doubek, P., Gool, L.: Bayesian Pixel Classification for Human Tracking. In: IEEE Workshop on Motion and Video Computing (MOTION 2005), Breckenridge, Colorado (2005)
Kim, K., Chalidabhongse, T.H., Harwood, D., Davis, L.: Background modeling and subtraction by codebook construction. In: IEEE International Conference on Image Processing (ICIP) (2004)
Elgammal, A., Harwood, D., Davis, L.: Non-Parametric Model for Background Subtraction. In: European Conference on Computer Vision, Dublin, Ireland (2000)
Chalidabhongse, T., Kim, K., Harwood, D., Davis, L.: A Perturbation Method for Evaluating Background Subtraction Algorithms. In: Int. Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, Beijing, China (2005)
Horprasert, T., Harwood, D., Davis, L.: A Statistical Approach for Real-time Robust Background Subtraction and Shadow Detection. In: IEEE ICCV 1999 Frame-Rate Workshop, Corfu, Greece (1999)
Andersen, P., Corlin, R.: Tracking of Interacting People and Their Body Parts for Outdoor Surveillance. Master’s thesis, Laboratory of Computer Vision and Media Technology, Aalborg University, Denmark (2005)
Gutchess, D., Trajkovic, M., Solal, E., Lyons, D., Jain, A.: A Background Model Initialization Algorithm for Video Surveillance. In: International Conference on Computer Vision, Vancouver, Canada (2001)
Wang, H., Suter, D.: Background Initialization with a New Robust Statistical Approach. In: Int. Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, Beijing, China (2005)
Kim, K., Chalidabhongse, T., Harwood, D., Davis, L.: Real-Time Foreground-Background Segmentation using Codebook Model. Real-Time Imaging 11 (2005)
Yang, C., Duraiswami, R., Davis, L.: Fast Multiple Object Tracking via a Hierarchical Particle Filter. In: International Conference on Computer Vision, Beijing, China (2005)
Mittal, A., Davis, L.S.: M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene. International Journal of Computer Vision 51, 189–203 (2003)
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Fihl, P., Corlin, R., Park, S., Moeslund, T.B., Trivedi, M.M. (2006). Tracking of Individuals in Very Long Video Sequences. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919476_7
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DOI: https://doi.org/10.1007/11919476_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48628-2
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