[en] The stationary background generation problem consists in generating a unique image representing the stationary background of a given video sequence. The LaBGen background generation method combines a pixel-wise median filter and a patch selection mechanism based on a motion detection performed by a background subtraction algorithm. In our previous works related to LaBGen, we have shown that, surprisingly, the frame difference algorithm provides the most effective motion detection on average. Compared to other background subtraction algorithms, it detects motion between two frames without relying on additional past frames, and is therefore memoryless. In this paper, we experimentally check whether the memoryless property is truly relevant for LaBGen, and whether the effective motion detection provided by the frame difference is not an isolated case. For this purpose, we introduce LaBGen-OF, a variant of LaBGen leverages memoryless dense optical flow algorithms for motion detection. Our experiments show that using a memoryless motion detector is an adequate choice for our background generation framework, and that LaBGen-OF outperforms LaBGen on the SBMnet dataset. We further provide an open-source C++ implementation of both methods at http://www.telecom.ulg.ac.be/labgen.
Centre de recherche :
Montefiore Institute - Montefiore Institute of Electrical Engineering and Computer Science - ULiège Telim
Laugraud, Benjamin ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Dép. d'électric., électron. et informat. (Inst.Montefiore)
Van Droogenbroeck, Marc ; Université de Liège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Langue du document :
Anglais
Titre :
Is a Memoryless Motion Detection Truly Relevant for Background Generation with LaBGen?
Date de publication/diffusion :
septembre 2017
Nom de la manifestation :
Advanced Concepts for Intelligent Vision Systems (ACIVS)
Agarwala, A., Dontcheva, M., Agrawala, M., Drucker, S., Colburn, A., Curless, B., Salesin, D., Cohen, M.: Interactive digital photomontage. ACM Trans. Graph. 23(3), 294-302 (2004)
Bouguet, J.-Y.: Pyramidal implementation of the affine Lucas Kanade feature tracker description of the algorithm. Intel Corporation 5(1-10), 4 (2001)
Bouwmans, T., Maddalena, L., Petrosino, A.: Scene background initialization: a taxonomy. Pattern Recognition Letters (in press)
De Gregorio, M., Giordano, M.: Background estimation by weightless neural networks. Pattern Recognition Letters (in press)
Farnebäck, G.: Two-frame motion estimation based on polynomial expansion. In: Bigun, J., Gustavsson, T. (eds.) SCIA 2003. LNCS, vol. 2749, pp. 363-370. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45103-X_50
Javed, S., Mahmmod, A., Bouwmans, T., Jung, S.K.: Motion-aware graph regularized RPCA for background modeling of complex scene. In: IEEE International Conference on Pattern Recognition (ICPR), IEEE Scene Background Modeling Contest (SBMC), Cancún, Mexico, pp. 120-125, December 2016
Jodoin, P.M., Maddalena, L., Petrosino, A., Wang, Y.: Extensive benchmark and survey of modeling methods for scene background initialization. IEEE Trans. Image Process. 26, 5244-5256 (2017)
Kroeger, T., Timofte, R., Dai, D., Van Gool, L.J.: Fast optical flow using dense inverse search. CoRR abs/1603.03590 (2016)
Laugraud, B., Piérard, S., Van Droogenbroeck, M.: LaBGen-P: a pixel-level stationary background generation method based on LaBGen. In: IEEE International Conference on Pattern Recognition (ICPR), IEEE Scene Background Modeling Contest (SBMC), Cancún, Mexico, pp. 107-113, December 2016
Laugraud, B., Piérard, S., Van Droogenbroeck, M.: LaBGen: a method based on motion detection for generating the background of a scene. Pattern Recognition Letters (in press)
Maddalena, L., Petrosino, A.: Background model initialization for static cameras. In: Background Modeling and Foreground Detection for Video Surveillance, pp. 3.1-3.16. Chapman and Hall/CRC (2014). Chap. 3
Maddalena, L., Petrosino, A.: Towards benchmarking scene background initialization. In: Murino, V., Puppo, E., Sona, D., Cristani, M., Sansone, C. (eds.) ICIAP 2015. LNCS, vol. 9281, pp. 469-476. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23222-5_57
Maddalena, L., Petrosino, A.: Extracting a background image by a multi-modal scene background model. In: IEEE International Conference on Pattern Recognition (ICPR), IEEE Scene Background Modeling Contest (SBMC), Cancún, Mexico, pp. 143-148, December 2016
McIvor, A.: Background subtraction techniques. In: Proceedings of the Image and Vision Computing, Auckland, New Zealand, November 2000
Reddy, V., Sanderson, C., Lovell, B.: A low-complexity algorithm for static background estimation from cluttered image sequences in surveillance contexts. EURASIP J. Image Video Process. 13 (2011). https://doi.org/10.1155/2011/164956
Sobral, A., Zahzah, E.H.: Matrix and tensor completion algorithms for background model initialization: a comparative evaluation. Pattern Recognition Letters (in press)
Sun, D., Roth, S., Black, M.J.: A quantitative analysis of current practices in optical flow estimation and the principles behind them. Int. J. Comput. Vis. 106(2), 115-137 (2014)
Wang, H., Suter, D.: A novel robust statistical method for background initialization and visual surveillance. In: Narayanan, P.J., Nayar, S.K., Shum, H.-Y. (eds.) ACCV 2006. LNCS, vol. 3851, pp. 328-337. Springer, Heidelberg (2006). https://doi.org/10.1007/11612032_34
Weinzaepfel, P., Revaud, J., Harchaoui, Z., Schmid, C.: DeepFlow: large displacement optical flow with deep matching. In: International Conference on Computer Vision (ICCV), Sydney, Australia, pp. 1385-1392, December 2013
Zach, C., Pock, T., Bischof, H.: A duality based approach for realtime TV-L1L1 optical flow. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 214-223. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74936-3_22