Abstract
This paper presents a knowledge-based framework for video analysis which systematically exploits relationship among analysis stages. A set of step-by-step feedback paths controls feedback generation and reception between consecutive analysis stages. An analysis ontology, which includes occurrences in the scene from high to very low semantic level, controls iterative decisions on every stage. As a result, both overall and intermediate analysis results are improved. This paper presents the framework and focuses on its application to foreground objects extraction. Experimental results show that the framework provides a richer low-level representation of the scene and improved short-term change detection and foreground detection masks.
Work supported by the Spanish Government (TEC2007-65400 - SemanticVideo), the Spanish Administration agency CDTI (CENIT-VISION 2007-1007) and the Comunidad de Madrid (S-0505/TIC-0223 - ProMultiDis-CM).
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References
Stauffer, C., Grimson, W.E.L.: Adaptive Background Mixture Models for Real-Time Tracking. In: Proc. CVPR 1999, p. 2246 (1999)
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)
Rymel, J., Renno, J., Greenhill, D., Orwell, J., Jones, G.A.: Adaptive eigen-backgrounds for object detection. In: Proc. ICIP 2004 (October 2004)
Piccardi, M., Jan, T.: Mean-shift background image modeling. In: Proc. ICIP 2004 (October 2004)
Piccardi, M.: Background subtraction techniques: a review. In: Proc. IEEE Intl. Conf. on Systems, Man and Cybernetics (October 2004)
Huwer, S., Niemann, H.: Adaptive Change Detection for Real-Time Surveillance Applications. In: IEEE Intl. Wksp. on Visual Surveillance (2000)
Desa, S.M., Salih, Q.A.: Image Subtraction for Real Time Moving Object Extraction. In: Proc. Intl. Conf. on Computer Graphics, Imaging and Visualization, CGIV 2004 (July 2004)
Li, L., Huang, W., Gu, I.Y.H., Tia, Q.: Foreground object detection from videos containing complex background. In: Proc. ACM Intl. Conf. on Multimedia (November 2002)
Simou, N., Tzouvaras, V., Avrithis, Y., Stamou, G., Kollias, S.: A Visual Descriptor Ontology for Multimedia Reasoning. In: Proc. of Wksp. on Image Analysis for Multimedia Interactive Services, WIAMIS 2005 (April 2005)
Mezaris, V., Kompatsiaris, I., Boulgouris, N., Strintzis, M.: Real-Time Compressed-Domain Spatiotemporal Segmentation and Ontologies for Video Indexing and Retrieval. In: IEEE Trans. on CSVT (May 2004)
Dasiopoulou, S., Mezaris, V., Kompatsiaris, I., Papastathis, V.K., Strintzis, M.G.: Knowledge-assisted semantic video object detection. In: IEEE Trans. on CSVT (October 2005)
Town, C.: Ontological inference for image and video analysis. Machine Vision and Applications 17(2) (2006)
Cucchiara, R., Grana, C., Piccardi, M., Prati, A.: Detecting moving objects, ghosts, and shadows in video streams. IEEE Trans. on PAMI (October 2003)
Rosin, P.: Thresholding for change detection. In: Proc. IEEE Intl. Conf. on Computer Vision (1998)
Prati, A., Mikic, I., Trivedi, M.M., Cucchiara, R.: Detecting moving shadows: algorithms and evaluation. IEEE Trans. on PAMI (July 2003)
VSSN 2006 Call for Algorithm Competition in Foreground/Background Segmentation (2006), http://mmc36.informatik.uni-augsburg.de/VSSN06_OSAC/
OpenCV, open source library for computer vision, http://www.intel.com/technology/computing/opencv/overview.htm
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García, A., Bescós, J. (2008). Video Object Segmentation Based on Feedback Schemes Guided by a Low-Level Scene Ontology. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2008. Lecture Notes in Computer Science, vol 5259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88458-3_29
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DOI: https://doi.org/10.1007/978-3-540-88458-3_29
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