Open Access Open Access  Restricted Access Subscription or Fee Access

An Interacting Multiple Model for Total Occlusion Handling


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irecos.v10i11.7352

Abstract


In this paper we address the problem of vision based human tracking in a video sequence captured by a stationary camera. The proposed system consists of two parts: human detection and tracking. Due to the fact that our main object to track has a non-rigid structure, a discriminative method for detection based on a start-structured human part model is used. For the tracking, in order to handle total occlusion, a new tracking method based on a two Kalman filters Interacting Multiple Model algorithm is used. The proposed system has been tested on both synthetic and real video datasets and the experiment results demonstrate that our method is able to obtain accurate estimates of the target's state when the image of the human being tracked is occluded during unknown time periods.
Copyright © 2015 Praise Worthy Prize - All rights reserved.

Keywords


Human Detection and Tracking; Deformable Human Part Model; Interacting Multiple Models; Total-Occlusion

Full Text:

PDF


References


Mukilan, P., Wahi, A., Optimal object detection and tracking using Improved Particle Swarm Optimization (IPSO), (2014) International Review on Computers and Software (IRECOS), 9 (4), pp. 638-649.

Mun Wai Lee, Ramakant Nevatia, Human Pose Tracking in Monocular Sequence Using Multilevel Structured, IEEE on pattern analysis and machine intelligence, vol, 31, no.1, January 2009.
http://dx.doi.org/10.1109/tpami.2008.35

Beymer, D., Konolige, K., Real-time tracking of multiple people using continuous detection, Proceedings of the ICCV-Frame Rate Workshop (1999).

Flavio, B., Window matching techniques with Kalman filtering or an improved object visual tracking, Proceedings of the IEEE Conference on Automation Science and Engineering (2007).
http://dx.doi.org/10.1109/coase.2007.4341822

G. Welch and G. Bishop, “Scaat: Incremental tracking with incom plete information,” in Proceedings of the 24th annual conference on Computer graphics and interactive techniques. ACM Press/Addison-Wesley Publishing Co., 1997, pp. 333–344.
http://dx.doi.org/10.1145/258734.258876

Niu, W. Jiao, Han, D., Wang, Y.F Real-time multiperson tracking in video surveillance . information communication and signal processing 1144-1148(2003).
http://dx.doi.org/10.1109/icics.2003.1292639

Piercarlo Dondi, Luca Lombardi, Luigi Cinque2, Multisubjects Tracking by Time-of-Flight Camera. ICIAP 2013, Part I, LNCS 8156, pp. 692–701, 2013. Springer-Verlag Berlin Heidelberg 2013
http://dx.doi.org/10.1007/978-3-642-41181-6_70

Weng, S.K., Kuo, C.M., Tu, S.K.: Video object tracking using adaptive Kalman filter. J. Vis. Commun. Image Represent. 17, 1190–1208 (2006).
http://dx.doi.org/10.1016/j.jvcir.2006.03.004

Chu, C.T., Hwang, J.N., Wang, S.Z., Chen, Y.Y.: Human track- ing by adaptive Kalman filtering and multiple kernels tracking with projected gradients. In: Proceedings of the Fifth ACM/IEEE International Conference on Distributed Smart Cameras, pp. 1–6 (2011).
http://dx.doi.org/10.1109/icdsc.2011.6042939

Khue Tra, Tuan V. Pham -Human fall detection based on adaptive background mixture model and HMM. The 2013 International Conference on Advanced Technologies for Communications (ATC'13). Electronic & Telecomm. Engineering. Dept. IEEE
http://dx.doi.org/10.1109/atc.2013.6698085

Khue Tra, Tuan V. Pham -Human fall detection based on adaptive background mixture model and HMM. The 2013 International Conference on Advanced Technologies for Communications (ATC'13). Electronic & Telecomm. Engineering. Dept. IEEE
http://dx.doi.org/10.1109/atc.2013.6698085

X.Ren, A.Berg, J.Malik, Recovering human body configurations using pairwise constraints between parts, CVPR,2005.
http://dx.doi.org/10.1109/iccv.2005.204

[surve1] M. Enzweiler and D. M. Gavrila, “Monocular pedestrian detection: Survey and experiments,” PAMI, vol. 31, no. 12, pp. 2179–2195, 2009.
http://dx.doi.org/10.1109/tpami.2008.260

Chris Stauffer W.E.L Grimson, Adaptive background mixture models for real-time tracking. The Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge,MA 02139.

Thulasimani, K., Srinivasagan, K.G., An analysis of object detection and tracking using recursive and non recursive algorithms for motion based video, (2013) International Review on Computers and Software (IRECOS), 8 (12), pp. 2909-2916.

L.Sigal, M.Black, Measure locally, reason globally: occlusion- sensitive articulated pose estimation, CVPR,2006.
http://dx.doi.org/10.1109/cvpr.2006.180

Swathikiran S., Sajith Sethu P., Human Detection and Tracking System for Automatic Video Surveillance, International Journal of Engineering Inventions e-ISSN: 2278-7461, p-ISSN: 2319-6491 Volume 2, Issue 10 (June 2013) PP: 58-61

D. M .Gavrilla, Pedestrian detection from a moving vehicle, ECCV ,2000.
http://dx.doi.org/10.1007/3-540-45053-x_3

S.Ioffe, D.Forsyth, Finding people by sampling, ICCV, 1999.
http://dx.doi.org/10.1109/iccv.1999.790398

M. Eichner , V. Ferrari, Better Appearance Models for Pictorial Structures, (2009) Proceedings of the British Machine Vision Conference (BMVC 2009), September 7-10, 2009.
http://dx.doi.org/10.5244/c.23.3

G.Hua , M. H.Yang,, Y. Wu , Learning to estimate human pose with data driven belief propagation, CVPR, 2005.
http://dx.doi.org/10.1109/cvpr.2005.208

D. M .Gavrilla, Pedestrian detection from a moving vehicle, ECCV ,2000.
http://dx.doi.org/10.1007/3-540-45053-x_3

P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan, Object detection with discriminatively trained part-based models, Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 32, No. 9, Sep. 2010.
http://dx.doi.org/10.1109/tpami.2009.167

Junge Zhang; Yinan Yu; Shuai Zheng; Kaiqi Huang, "An empirical study of visual features for part based model," in Pattern Recognition (ACPR), 2011 First Asian Conference on , vol., no., pp.219-223, 28-28 Nov. 2011.
http://dx.doi.org/10.1109/acpr.2011.6166532

M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes (VOC) challenge. IJCV, 88(2), 2010.
http://dx.doi.org/10.1007/s11263-009-0275-4

N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005.
http://dx.doi.org/10.1109/cvpr.2005.177

Schwartz, W.R.; Kembhavi, A.; Harwood, D.; Davis, L.S., "Human detection using partial least squares analysis," Computer Vision, 2009 IEEE 12th International Conference on , vol., no., pp.24,31, Sept. 29 2009-Oct. 2 2009
http://dx.doi.org/10.1109/iccv.2009.5459205

Van-Dung Hoang; Vavilin, A.; Kang-Hyun Jo, "Fast human detection based on parallelogram haar-like features," in IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society , vol., no., pp.4220-4225, 25-28 Oct. 2012.
http://dx.doi.org/10.1109/iecon.2012.6389212

Y.Bar-Shalom, XR. Li, T.Kirubarajan. "Estimation with applications to Tracking and Navigation", New York: Wiley; 2001.
http://dx.doi.org/10.1002/0471221279

Rezai-Rad, G.-A., Ebrahimi, R.V., A new method for multiple target tracking based on fuzzy possibility, (2013) International Review on Modelling and Simulations (IREMOS), 6 (1), pp. 300-310.

Ferdowsi, M.H., Passive range estimation using two and three optical cameras, (2013) International Review on Modelling and Simulations (IREMOS), 6 (2), pp. 613-618.

Thulasimani, K., Srinivasagan, K.G., An analysis of object detection and tracking using recursive and non recursive algorithms for motion based video, (2013) International Review on Computers and Software (IRECOS), 8 (12), pp. 2909-2916.


Refbacks



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize