Background subtraction techniques: Systematic evaluation and comparative analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5807 LNCS, Issue , 2009, Pages 33-42

  Herrero, Sonsoles ^a   Bescós, Jesús ^a  

^a UNIVERSIDAD AUTÓNOMA DE MADRID   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

BACKGROUND SUBTRACTION; BACKGROUND SUBTRACTION TECHNIQUES; COMPARATIVE ANALYSIS; COMPUTER VISION APPLICATIONS; CRITICAL TASKS; MOVING-OBJECT DETECTION; STATIC CAMERAS; SYSTEMATIC EVALUATION; TRUTH DATA; VIDEO SEQUENCES;

COMPUTER APPLICATIONS; VIDEO RECORDING;

COMPUTER VISION;

EID: 70549106164     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04697-1_4     Document Type: Conference Paper

References (18)

1. Cristani, M., Bicego, M., Murino, V.: Multi-level background initialization using Hidden Markov Models. In: First ACM SIGMM Int. workshop on Video surveillance, pp. 11-20 (2003)
2. Piccardi, M.: Background subtraction techniques: a review. In: SMC 2004, vol. 4, pp. 3099-3104 (2004)
3. Cheung, S.-C., Kamath, C.: Robust techniques for background subtraction in urban traffic video. In: Panchanathan, S., Vasudev, B. (eds.) Proc. Elect Imaging: Visual Comm. Image Proce. (Part One) SPIE, vol. 5308, pp. 881-892 (2004)
4. Cucchiara, R.: People Surveillance, VISMAC Palermo (2006)
5. Ewerth, R., Freisleben, B.: Frame difference normalization: an approach to reduce error rates of cut detection algorithms for MPEG videos. In: ICIP, pp. 1009-1012 (2003)
6. Tang, Z., Miao, Z., Wan, Y.: Background Subtraction Using Running Gaussian Average and Frame Difference. In: Ma, L., Rauterberg, M., Nakatsu, R. (eds.) ICEC 2007. LNCS, vol. 4740, pp. 411-414. Springer, Heidelberg (2007)
7. Wren, A., Darrell, P.: Pfinder: Real-time tracking of the human body. PAMI (1997)
8. Cavallaro, A., Steiger, O., Ebrahimi, T.: Semantic video analysis for adaptive content delivery and automatic description. IEEE Transactions on Circuits and Systems for Video Technology 15(10), 1200-1209 (2005)
9. Stauffer, G.: Adaptive background mixture models for real-time tracking. In: CVPR (1999)
10. Carminati, L., Benois-Pineau, J.: Gaussian mixture classification for moving object detection in video surveillance environment. In: ICIP, pp. 113-116 (2005)
11. Comaniciu, D.: Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and machine Intelligence 24(5), 603 (2002)
12. Elgammal, A.M., Harwood, D., Davis, L.S.: Non-parametric model for background subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 751-767. Springer, Heidelberg (2000)
13. Zivkovic, Z., Van Der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recognition Letters 27(7), 773-780 (2006)
14. Stenger, B., Ramesh, V., Paragios, N., Coetzee, F., Buhmann, J.M.: Topology Free Hidden Markov Models: Application to Background Modeling. In: Eighth Int. Conf. on Computer Vision, ICCV 2001, vol. 1, pp. 294-301 (2001)
15. Mittal, A., Paragios, N.: Motion-based background subtraction using adaptive kernel density estimation. In: Proceedings of the Int. Conf. Comp. Vision and Patt. Recog., CVPR, pp. 302-309 (2004)
16. Tiburzi, F., Escudero, M., Bescós, J., Martínez, J.M.: A Corpus for Motion-based Video-object Segmentation. In: IEEE International Conference on Image Processing (Workshop on Multimedia Information Retrieval), ICIP 2008, SanDiego, USA (2008)
17. El Baf, F., Bouwmans, T., Vachon, B.: Comparison of Background Subtraction Methods for a Multimedia Application. In: 14th International Conference on systems, Signals and Image Processing, IWSSIP 2007, Maribor, Slovenia, pp. 385-388 (2007)
18. Parks, D.H., Fels, S.S.: Evaluation of Background Subtraction Algorithms with PostProcessing. In: IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance, AVSS 2008, pp. 192-199 (2008)