Abstract
The detection of moving objects under a free-moving camera is a difficult problem because the camera and object motions are mixed together and the objects are often detected into the separated components. To tackle this problem, we propose a fast moving object detection method using optical flow clustering and Delaunay triangulation as follows. First, we extract the corner feature points using Harris corner detector and compute optical flow vectors at the extracted corner feature points. Second, we cluster the optical flow vectors using K-means clustering method and reject the outlier feature points using Random Sample Consensus algorithm. Third, we classify each cluster into the camera and object motion using its scatteredness of optical flow vectors. Fourth, we compensate the camera motion using the multi-resolution block-based motion propagation method and detect the objects using the background subtraction between the previous frame and the motion compensated current frame. Finally, we merge the separately detected objects using Delaunay triangulation. The experimental results using Carnegie Mellon University database show that the proposed moving object detection method outperforms the existing other methods in terms of detection accuracy and processing time.
Similar content being viewed by others
References
Bajpai N (2010) Business statistics. Pearson
Barnich O, Droogenbroeck MV (2009) VIBE: a powerful random technique to estimate the background in video sequences. In: Proc. IEEE ICASSP 2009, pp 945–948
Borshukov GD, Bozdagi G, Altunbasak Y, Tekalp AM (1997) Motion segmentation by multistage affine classification. IEEE Trans Image Process 6(11):1591–1594
Bouguet JY (2000) Pyramidal implementation of the Lucas Kanade feature tracker description of the algorithm. OpenCV Documentation
Chen M, Gonzalez S, Cao H, Zhang Y, Vuong S (2010) Enabling low bit-rate and reliable video surveillance over practical wireless sensor networks. J. Supercomput. doi:10.1007/s11227-010-0475-2
Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley-Interscience 2001
Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395
Guibas LJ, Knuth DE, Sharir M (1992) Randomized incremental construction of Delaunay and Voronoi diagrams. Algorithmica 7(4):381–413
Han B, Comaniciu D, Zhu Y, Davis LS (2008) Sequential kernel density approximation and its application to real-time visual tracking. IEEE Trans Pattern Anal Mach Intell 30(7):1186–1197
Hayman E, Eklundh J (2003) Statistical background subtraction for a mobile observer. In: Proc. IEEE ICCV 2003, pp 67–74
Jin Y, Tao L, Di H, Rao NI, Xu G (2008) Background modeling from a free-moving camera by multi-layer homography algorithm. In: Proc. IEEE ICIP 2008
Ke Q, Kanade T (2001) A subspace approach to layer extraction. In: Proc. IEEE CVPR 2001
Li L, Huang W, Gu IYH, Tian Q (2003) Foreground object detection from videos containing complex background. In: Proc. ACMMM 2003
Maddalena L, Petrosino A (2008) A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans Image Process 17(7):1168–1177
Mittal A, Hunttenlocher D (2000) Scene modeling for wide area surveillance and image synthesis. In: Proc. IEEE CVPR 2000, pp 160–167
Oliver NM, Rosario B, Pentland AP (2000) A Bayesian computer vision system for modeling human interactions. IEEE Trans Pattern Anal Mach Intell 22(8):831–843
Ren X, Malik J (2007) Tracking as repeated figure/ground segmentation. In: Proc. IEEE CVPR 2007, pp 1–8
Ren Y, Chua CS, Ho YK (2003) Statistical background modeling for non-stationary camera. Pattern Recogn 24(1–3):183–196
Ren X, Song J, Ying H, Zhu Y, Qiu X (2007) Robust nose detection and tracking using GentleBoost and improved Lucas–Kanade optical flow algorithms. In: Proc. IEEE ICIC 2007, pp 1240–1246
Sand P, Teller S (2006) Particle video: long-range motion estimation using point trajectories. In: Proc. IEEE CVPR 2006, pp 2195–2202
Schmid C, Mohr R, Bauckhage C (2000) Evaluation of interest point detectors. Int J Comput Vis 37(2):151–172
Schoenemann T, Cremers D (2008) High resolution motion layer decomposition using dual-space graph cuts. In: Proc. IEEE CVPR 2008, pp 1–7
Sheikh Y, Javed O, Kanade T (2009) Background subtraction for freely moving cameras. In: Proc. IEEE ICCV 2009, pp 1219–1225
Shi J, Tomasi C (1994) Good features to track. In: Proc. IEEE CVPR 1994, pp 593–600
Tao H, Sawhney HS, Kumar R (2002) Object tracking with bayesian estimation of dynamic layer representations. IEEE Trans Pattern Anal Mach Intell 24(1):75–89
Uemura H, Ishikawa S, Mikolajczyk K (2008) Feature tracking and motion compensation for action recognition. In: Proc. BMVC 2008
Xiao J, Shah M (2005) Motion layer extraction in the presence of occlusion using graph cuts. IEEE Trans Pattern Anal Mach Intell 27(10):1644–1659
Zivkovic Z, Heijden F (2006) Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn Lett 27(7):773–780
Acknowledgements
This work was supported by the MKE (The Ministry of Knowledge Economy), Korea,under the Core Technology Development for Breakthrough of Robot Vision Research support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2010-C7000-1001-0006). And this research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology (No. 2011-0027953).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kim, J., Wang, X., Wang, H. et al. Fast moving object detection with non-stationary background. Multimed Tools Appl 67, 311–335 (2013). https://doi.org/10.1007/s11042-012-1075-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-012-1075-3