Background Subtraction Methods in Video Streams: A Review
Abstract
Background subtraction is one of the most important parts in image and video processing field. There are some unnecessary parts during the image or video processing, and should be removed, because they lead to more execution time or required memory. Several subtraction methods have been presented for the time being, but find the best-suited method is an issue, which this study is going to address. Furthermore, each process needs to the specific subtraction technique, and knowing this issue helps researchers to achieve faster and higher performance in their research. This paper presents a comparative study of several existing background subtraction methods which have been investigated from simple background subtraction to more complex statistical techniques. The goal of this study is to provide a view of the strengths and drawbacks of the widely used methods. The methods are compared based on their memory requirement, the computational time and their robustness of different videos. Finally, a comparison between the existing methods has been employed with some factors like computational time or memory requirements. It is also hoped that this analysis helps researchers to address the difficulty of selecting the most convenient method for background subtraction.
Keywords
References
Benezeth, Y., Jodoin, P.-M., Emile, B., Laurent, H. and Rosenberger, C. (2010). Comparative study of background subtraction algorithms. Journal of Electronic Imaging, 19, 033003-033003-12.
Boult, T., Micheals, R., Gao, X., Lewis, P., Power, C., Yin, W. and Erkan, A. (1999). Frame-rate omnidirectional surveillance and tracking of camouflaged and occluded targets. Visual Surveillance, 1999. Second IEEE Workshop on,(VS'99), 48-55.
Bouwmans, T., El Baf, F. and Vachon, B. (2008). Background modeling using mixture of gaussians for foreground detection-a survey. Recent Patents on Computer Science, 1, 219-237.
Brutzer, S., Höferlin, B. and Heidemann, G. (2011). Evaluation of background subtraction techniques for video surveillance. Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, 1937-1944.
Calderara, S., Melli, R., Prati, A. and Cucchiara, R. (2006). Reliable background suppression for complex scenes. Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks, 211-214.
Cucchiara, R., Grana, C., Piccardi, M. and Prati, A. (2003). Detecting moving objects, ghosts, and shadows in video streams. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 25, 1337-1342.
Cucchiara, R., Grana, C., Prati, A. and Vezzani, R. (2005). Probabilistic posture classification for human-behavior analysis. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 35, 42-54.
Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society. Series B (methodological), 1-38.
François, A. R. and Medioni, G. G. (Year). Adaptive color background modeling for real-time segmentation of video streams. Proceedings of the International Conference on Imaging Science, Systems, and Technology, 1999. 227-232.
Godbehere, A. B., Matsukawa, A. and Goldberg, K. (2012). Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation. American Control Conference (ACC), 2012, 4305-4312.
Goyat, Y., Chateau, T., Malaterre, L. and Trassoudaine, L. (2006). Vehicle trajectories evaluation by static video sensors. Intelligent Transportation Systems Conference, 2006. ITSC'06. IEEE, 864-869.
Halevy, G. and Weinshall, D. (1999). Motion of disturbances: detection and tracking of multi-body non-rigid motion. Machine Vision and Applications, 11, 122-137.
Han, B., Comaniciu, D. and Davis, L. (2004). Sequential kernel density approximation through mode propagation: applications to background modeling. proc. ACCV, 4, 818-823.
Haritaoglu, I., Harwood, D. and Davis, L. S. (2000). W 4: Real-time surveillance of people and their activities. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22, 809-830.
Heikkilä, J. and Silvén, O. (2004). A real-time system for monitoring of cyclists and pedestrians. Image and Vision Computing, 22, 563-570.
Hofmann, M., Tiefenbacher, P. and Rigoll, G. (2012). Background segmentation with feedback: The pixel-based adaptive segmenter. Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on, 38-43.
Huwer, S. and Niemann, H. (2000). Adaptive change detection for real-time surveillance applications. Visual Surveillance, 2000. Proceedings. Third IEEE International Workshop on, 37-46.
Koller, D., Weber, J., Huang, T., Malik, J., Ogasawara, G., Rao, B. and Russell, S. (1994). Towards robust automatic traffic scene analysis in real-time. Pattern Recognition, 1994. Vol. 1-Conference A: Computer Vision & Image Processing., Proceedings of the 12th IAPR International Conference on, 1, 126-131.
Luque, R. M., DomÃnguez, E., Palomo, E. J. and Muñoz, J. (2008). A neural network approach for video object segmentation in traffic surveillance. Image Analysis and Recognition. Springer. 151-158.
McFarlane, N. J. and Schofield, C. P. (1995). Segmentation and tracking of piglets in images. Machine vision and applications, 8, 187-193.
Mittal, A. and Paragios, N. (2004). Motion-based background subtraction using adaptive kernel density estimation. Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, 2, II-302-II-309 Vol. 2.
Mohamad, A. and Osman, M. (2013). Adaptive median filter background subtractions technique using fuzzy logic. Computing, Electrical and Electronics Engineering (ICCEEE), 2013 International Conference on, 115-120.
Montacié, C., Caratay, M.-J. and Barras, C. (1996). Mixture splitting technique and temporal control in a HMM-based recognition system. Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on, 2, 977-980.
Naraghi, N. S. (2009). A comparative study of background estimation algorithms. Eastern Mediterranean University.
Oliver, N. M., Rosario, B. and Pentland, A. P. (2000). A Bayesian computer vision system for modeling human interactions. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22, 831-843.
Piccardi, M. (2004). Background subtraction techniques: a review. Systems, man and cybernetics, 2004 IEEE international conference on, 4, 3099-3104.
Pong, P. and Bowden, R. (2002). An improved adaptive background mixture model for real-time tracking with shadow detection. Video-based surveillance systems. Springer. 135-144.
Radke, R. J., Andra, S., Al-Kofahi, O. and Roysam, B. (2005). Image change detection algorithms: a systematic survey. Image Processing, IEEE Transactions on, 14, 294-307.
Rai, N. K., Chourasia, S. and Sethi, A. (2013). An efficient neural network based background subtraction method. Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), 453-460.
Sebastian, P., Vooi Voon, Y. and Comley, R. (2011). Performance Evaluation Metrics for Video Tracking. IETE Technical Review, 28, 493-502.
Sen-Ching, S. C. and Kamath, C. (2004). Robust techniques for background subtraction in urban traffic video. Electronic Imaging 2004, 881-892.
Stauffer, C. and Grimson, W. E. L. (1999). Adaptive background mixture models for real-time tracking. Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., 2.
Tamersoy, B. (2009). Background Subtraction–Lecture Notes. University of Texas at Austin.
Tian, Y., Wang, Y., Hu, Z. and Huang, T. (2013). Selective eigenbackground for background modeling and subtraction in crowded scenes. Circuits and Systems for Video Technology, IEEE Transactions on, 23, 1849-1864.
Tripathi, A. K. and Mukhopadhyay, S. (2012). Removal of fog from images: A review. IETE Technical Review, 29, 148-156.
Wren, C. R., Azarbayejani, A., Darrell, T. and Pentland, A. P. (1997). Pfinder: Real-time tracking of the human body. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 19, 780-785.
Zhang, H. and Xu, D. (2006). Fusing color and texture features for background model. Fuzzy Systems and Knowledge Discovery: Third International Conference, FSKD 2006, Xi’an, China, September 24-28, 2006. Proceedings, 887-893.
Zhao, Z., Bouwmans, T., Zhang, X. and Fang, Y. (2012). A fuzzy background modeling approach for motion detection in dynamic backgrounds. Multimedia and Signal Processing. Springer. 177-185.
Zhou, Q. and Aggarwal, J. K. (2001). Tracking and classifying moving objects from video. Proceedings of IEEE Workshop on Performance Evaluation of Tracking and Surveillance, 46-54.
Zivkovic, Z. (2004). Improved adaptive Gaussian mixture model for background subtraction. Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, 2, 28-31.
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