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Abandoned Object Detection using Temporal Consistency Modeling
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 164 - Number 10 |
| Year of Publication: 2017 |
| Authors: Divya C. Patil, Pravin S. Patil |
10.5120/ijca2017913707
|
Divya C. Patil, Pravin S. Patil . Abandoned Object Detection using Temporal Consistency Modeling. International Journal of Computer Applications. 164, 10 ( Apr 2017), 15-21. DOI=10.5120/ijca2017913707
Abstract
In this paper an effective approach for detecting the abandoned object/ luggage for video surveillance is present. Here the long-term and short-term background models are combined to extract foreground objects, where each pixel in an input is classified as two bit code. To identify the static foreground regions, a framework is used based on the temporal transition of code pattern and it also determines whether the candidate regions contain the abandoned object by analyzing the back traced trajectories of luggage owner. This paper also introduces the real-time application of proposed method. The real-time application is performed by using raspberry-pi processor and the raspberry-pi camera. The experimental results show that, the proposed approach is effective for detecting abandoned object/ luggage.
References
- J. Martínez-del-Rincón, J. E. Herrero-Jaraba, J. R. Gómez, and C. Orrite-Urunuela, “Automatic left luggage detection and tracking using multi-camera UKF,” in Proc. IEEE 9th IEEE Int. Workshop PETS,
- Jun. 2006, pp. 59–66.
- F. Porikli, Y. Ivanov, and T. Haga, “Robust abandoned object detection using dual foregrounds,” EURASIP J. Adv. Signal Process., vol. 2008, Jan. 2008, Art. ID 30.
- R. H. Evangelio, T. Senst, and T. Sikora, “Detection of static objects for the task of video surveillance,” in Proc. IEEE WACV, Jan. 2011, pp. 534–540.
- Y. Tian, R. S. Feris, H. Liu, A. Hampapur, and M.-T. Sun, “Robust detection of abandoned and removed objects in complex surveillance videos,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 41, no. 5, pp. 565–576, Sep. 2011.
- Q. Fan and S. Pankanti, “Modeling of temporarily static objects for robust abandoned object detection in urban surveillance,” in Proc. 8thA IEEE Int. Conf. AVSS, Aug./Sep. 2011, pp. 36–41.
- Q. Fan, P. Gabbur, and S. Pankanti, “Relative attributes for largescale abandoned object detection,” in Proc. IEEE ICCV, Dec. 2013, pp. 2736–2743.
- H.-H. Liao, J.-Y. Chang, and L.-G. Chen, “A localized approach to abandoned luggage detection with foreground-mask sampling,” in Proc. IEEE 5th Int. Conf. AVSS, Sep. 2008, pp. 132–139.
- J. Pan, Q. Fan, and S. Pankanti, “Robust abandoned object detection using region-level analysis,” in Proc. 18th IEEE ICIP, Sep. 2011, pp. 3597–3600.
- F. Lv, X. Song, B. Wu, V. K. Singh, and R. Nevatia, “Left-luggage detection using Bayesian inference,” in Proc. IEEE Int. Workshop PETS, 2006, pp. 83–90.
- L. Li, R. Luo, R. Ma, W. Huang, and K. Leman, “Evaluation of an IVS system for abandoned object detection on PETS 2006 datasets,” in Proc. IEEE Workshop PETS, 2006, pp. 91–98.
- E. Auvinet, E. Grossmann, C. Rougier, M. Dahmane, and J. Meunier, “Left-luggage detection using homographies and simple heuristics,” in Proc. 9th IEEE Int. Workshop PETS, 2006, pp. 51–58.
- 1370 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 10, NO. 7, JULY 2015
- C. Stauffer and W. E. L. Grimson, “Adaptive background mixture models for real-time tracking,” in Proc. IEEE Comput. Soc. Conf. CVPR, vol. 2. Jun. 1999, pp. 246–252.
- K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, “Realtime foreground-background segmentation using codebook model,” Real-Time Imag., vol. 11, no. 3, pp. 172–185, 2005.
- Z. Zivkovic, “Improved adaptive Gaussian mixture model for background subtraction,” in Proc. 17th ICPR, 2004, pp. 28–31.
- Y.-T. Chen, C.-S. Chen, C.-R. Huang, and Y.-P. Hung, “Efficient hierarchical method for background subtraction,” Pattern Recognit., vol. 40, no. 10, pp. 2706–2715, 2007.
- P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 9, pp. 1627–1645, Sep. 2010.
- C. Dubout and F. Fleuret, “Exact acceleration of linear object detectors,” in Proc. 12th ECCV, 2012, pp. 301–311.
- PETS 2006 Dataset. [Online]. Available: http://www.cvg.reading.ac.uk/ PETS2006/data.html, accessed Mar. 17, 2015.
- S. Agarwal, A. Awan, and D. Roth, “Learning to detect objects in images via a sparse, part-based representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 11, pp. 1475–1490, Nov. 2004.
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