Abstract
Today, tracking of moving objects in video becomes a highlight in multimedia. This paper proposes a novel method, which is suitable for applying on relatively high-resolution videos that moving objects can be distinguished from their color and shape information. This method matches and tracks multiple moving objects in video by extracting and combining multi-features. With the background reconstruction method we proposed, the moving objects are separated as sub images from the background, we first extract some valuable features from each sub image, especially the topological information. Then, features are applied to a strong classifier which is accumulated with weak feature classifiers. After that, by the initial matching of moving objects, we extract their kinematical features to reinforce the matching method. Finally, experimental results show the effectiveness of the novel algorithm.
Similar content being viewed by others
References
Aggarwal JK, Cai Q (1997) Human motion analysis: a review [C] //nonrigid and articulated motion workshop. Proc IEEE IEEE 1997:90–102
Bai Y, Tang M (2014) Robust visual tracking via augmented kernel SVM[J]. Image Vis Comput 32(8):465–475
Chen P, Qian H, Wang W et al (2011) Contour tracking using gaussian particle filter [J]. IET Image Process 5(5):440–447
Chen Q, Sun QS, Heng PA et al (2010) Two-stage object tracking method based on kernel and active contour [J]. Circ Syst Video Tech, IEEE Trans 20(4):605–609
Chorianopoulos K (2013) Collective intelligence within web video [J]. Human-Centric Comput Inf Sci 3(1):1–16
Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking [J]. Pattern Anal Mach Int, IEEE Trans 25(5):564–577
Deb K (2014) Multi-objective optimization [M] //Search methodologies. Springer, US, pp 403–449
Einicke GA, White LB (1999) Robust extended Kalman filtering [J]. IEEE Trans Signal Process 47(9):2596–2599
Foroughi H, Aski BS (2008) Pourreza H. Intelligent video surveillance for monitoring fall detection of elderly in home environments[C]//Computer and Information Technology, 2008. ICCIT 2008. 11th International Conference on. IEEE: 219–224
Fu W, Xu Z, Liu S et al (2011) The capture of moving object in video image [J]. J Multimed 6(6):518–525
Fu W, Zhou J, Liu S et al (2014) Differential trajectory tracking with automatic learning of background reconstruction [J]. Multimed Tools Appl. doi:10.1007/ s11042-014-2391-6
Godec M, Roth PM, Bischof H (2013) Hough-based tracking of non-rigid objects [J]. Comput Vis Image Underst 117(10):1245–1256
Goswami K, Hong GS, Kim BG (2013) A novel mesh-based moving object detection technique in video sequence [J]. J Converg 4(3):20–24
Hayes GR (2011) The relationship of action research to human-computer interaction [J]. ACM Trans Comput-Human Inter 18(3):15
Huang C, Wang S (2010) A cascaded hierarchical framework for moving object detection and tracking [C] //Image Processing (ICIP), 2010 17th IEEE International Conference on. IEEE: 4629–4632
Jia X, Lu H, Yang MH (2012) Visual tracking via adaptive structural local sparse appearance model [C] //Computer Vision and Pattern Recognition (CVPR), 2012 I.E. Conference on. IEEE: 1822–1829
Julier SJ, Uhlmann JK (2004) Unscented filtering and nonlinear estimation [J]. Proc IEEE 92(3):401–422
Kalal Z, Mikolajczyk K, Matas J (2012) Tracking-learning-detection [J]. Pattern Anal Mach Int, IEEE Trans 34(7):1409–1422
Kalman RE (1960) A new approach to linear filtering and prediction problems [J]. J Fluids Eng 82(1):35–45
Kim DY, Jeon M (2013) Spatio-temporal auxiliary particle filtering with-norm-based appearance model learning for robust visual tracking [J]. Image Proc, IEEE Trans 22(2):511–522
Kim H, Lee SH, Sohn MK et al (2014) Illumination invariant head pose estimation using random forests classifier and binary pattern run length matrix [J]. Human-Centric Comput Inf Sci 4(1):1–12
Kwak S, Nam W, Han B et al (2011) Learning occlusion with likelihoods for visual tracking [C] //Computer Vision (ICCV), 2011 I.E. International Conference on. IEEE: 1551–1558
Lee Hung Liew LHL, Beng Yong Lee BYL, Beng Yong Lee BYL et al (2013) Aerial images rectification using non-parametric approach [J]. J Converg 4(1):15–22
Li G, Qin L, Huang Q et al (2011) Treat samples differently: Object tracking with semi-supervised online CovBoost [C] //Computer Vision (ICCV), 2011 I.E. International Conference on. IEEE: 627–634
Liu S, Fu W, Zhao W et al (2013) A novel fusion method by static and moving facial capture [J]. Math Probl Eng. doi:10.1155/2013/503924
Liu B, Huang J, Yang L et al (2011) Robust tracking using local sparse appearance model and k-selection [C] //Computer Vision and Pattern Recognition (CVPR), 2011 I.E. Conference on. IEEE: 1313–1320
Mei X, Ling H (2009) Robust visual tracking using L1 minimization [C] //Computer Vision (ICCV), 2009 I.E. 12th International Conference on. IEEE: 1436–1443
Oron S, Bar-Hillel A, Levi D et al (2012) Locally orderless tracking [C] //Computer Vision and Pattern Recognition (CVPR), 2012 I.E. Conference on. IEEE: 1940–1947
Stalder S, Grabner H, Van Gool L (2010) Cascaded confidence filtering for improved tracking-by-detection [M] //Computer vision–ECCV 2010. Springer, Berlin, pp 369–382
Uddin J, Islam R, Kim JM (2014) Texture feature extraction techniques for fault diagnosis of induction motors [J]. J Converg 5(2):15–20
Vezzani R, Cucchiara R (2010) Video surveillance online repository (visor): an integrated framework [J]. Multimed Tools Appl 50(2):359–380
Vipparthi SK, Nagar SK (2014) Color directional local quinary patterns for content based indexing and retrieval [J]. Human-Centric Comput Inf Sci 4(1):1–13
Wang S, Lu H, Yang F et al (2011) Superpixel tracking [C] //Computer Vision (ICCV), 2011 I.E. International Conference on. IEEE: 1323–1330
Wu Y, Ling H, Yu J et al (2011) Blurred target tracking by blur-driven tracker [C] //Computer Vision (ICCV), 2011 I.E. International Conference on. IEEE: 1100–1107
Zhang T, Ghanem B, Liu S et al (2012) Robust visual tracking via multi-task sparse learning[C] //Computer Vision and Pattern Recognition (CVPR), 2012 I.E. Conference on. IEEE: 2042–2049
Zhang X, Hu W, Qu W et al (2010) Multiple object tracking via species-based particle swarm optimization [J]. Circ Syst Video Tech, IEEE Trans 20(11):1590–1602
Zhong W, Lu H, Yang MH (2012) Robust object tracking via sparsity-based collaborative model [C] //Computer Vision and Pattern Recognition (CVPR), 2012 I.E. Conference on. IEEE: 1838–1845
Zhu J, Lao Y, Zheng YF (2010) Object tracking in structured environments for video surveillance applications [J]. Circ Syst Video Tech, IEEE Trans 20(2):223–235
Acknowledgments
This work is supported by National Natural Science Foundation of China [No. 61262082, 61461039], Key Project of Chinese Ministry of Education [No.212025], Inner Mongolia Science Foundation for Distinguished Young Scholars [2012JQ03], Program of Higher-level talents of Inner Mongolia University [125130], Postgraduate Scientific Research Innovation Foundation of Inner Mongolia [B20141012610Z].
The authors would like to express their heartfelt gratitude to all the volunteers in the experiments and the anonymous reviewers, for their help on this paper.
Conflict of interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Fu, W., Zhou, J. & Ma, Y. Moving tracking with approximate topological isomorphism. Multimed Tools Appl 75, 15553–15570 (2016). https://doi.org/10.1007/s11042-015-2519-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-015-2519-3