IJPTT

Development of Abandoned Object Detection Based on Region of Interest Method

	International Journal of Computer & Organization Trends  (IJCOT)
	© 2017 by IJCOT Journal
	Volume - 8 Issue - 6
	Year of Publication : 2018
	Authors :  Dr.V.Vivek, Dr.P.Senthil Pandian, S.Durai Pandi, B.Sivananthan, K.Peer Mohammed

Citation

MLA Style:Dr.V.Vivek, Dr.P.Senthil Pandian, S.Durai Pandi, B.Sivananthan, K.Peer Mohammed "Development of Abandoned Object Detection Based on Region of Interest Method" International Journal of Computer and Organization Trends 8.6 (2018): 1-6.

APA Style:Dr.V.Vivek, Dr.P.Senthil Pandian, S.Durai Pandi, B.Sivananthan, K.Peer Mohammed (2018). Development of Abandoned Object Detection Based on Region of Interest Method. International Journal of Computer and Organization Trends, 8(6), 1-6.

Abstract

Abandoned object detection is a vital demand in several video police work contexts. This implementation is easy beside reusable in contrast to existing techniques. Video segmentation is completed that converts the input video into range of frames. Objects foreign to a usual surroundings are extracted victimisation background subtraction. The Region of Interest (ROI) is extracted, so eliminating video are that are unlikely to contain abandoned objects. The method of blob detection computes statistics of the thing. Morphological shut system is formed to fill in tiny gaps within the detected objects. This paper is employed to observe abandoned and purloined objects. the main target is to work out static regions that have recently modified within the prospect by activity background subtraction. The projected work will observe abandoned objects beside is capable of activity this in period of time and provides additional correct results.

References

[1] C.Hettiarachchi, Asitha Nanayakkara, Ayesha Dissanayaka, Charith Wijenayake, Chathura De Silva, “Abandoned object detection with logical reasoning” IEEE International Advance Computing Conference (IACC) 2014.
[2] Chen, C.-C., Aggarwal, J.K.: An adaptive background model initialization algorithm with objects moving at different depths. In: IEEE International Conference on Image Processing (ICIP), (2008).
[3] Álvaro Bayona, Juan C. SanMiguel, José M.Martinez, “Stationary foreground detection using background subtraction and temporal difference in video surveillance” 17th IEEE International Conference on Image Processing, ICIP 2010, IEEE 2010.
[4] Massimo Piccardi, “Background Subtraction technique- a review” IEEE International Conference on Systems, Man and Cybernetics,2004.
[5] V.D.Shet, . D. Harwood and L. S. Davis, "VidMAP: Video Monitoring of Activity with Prolog," in IEEE Conference on Advanced Video and Signal Based Surveillance, 2005.
[6] Gutchess, D., Trajkovic, M., Kohen-Solal, E., Lyons, D.,Jain, A.K.: A Background model initialization algorithm for video surveillance. In: Proceedings of IEEE International Conference on Computer Vision (ICCV), pp. 733–740 (2001)
[7] Lv, F., Song, X., Wu, B., Singh, V.K., Nevatia, R.: Left-luggage detection using Bayesian inference. In: Proceedings of IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), New York, pp. 83–90 (2006)
[8] Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 22(8), 747–757 (2000)
[9] Smith, K., Quelhas, P. Gatica-Perez: Detecting abandoned luggage items in a public space. In: Proceedings of IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), New York, pp. 75–82 (2006)
[10] Bhargava,M., Chen, C.-C., Ryoo, M.S., Aggarwal, J.K.: Detection of abandoned objects in crowded environments. In: Proceedings of 2007 IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS), London (2007)
[11] Grabner, H., Roth, P., Grabner, M.: Autonomous learning of a robust background model for change detection. In: Proceedings of IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), New York,pp. 39–54 (2006)
[12] Li, L., Luo, R., Huang, W., Eng, H.: Context-controlled adaptive background subtraction. In: Proceedings of IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), New York, pp. 31–38 (2006)
[13] Porikli, F: Detection of temporal static regions by processing video at different frame rates. In: Proceedings of IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS), London (2007)
[14] Auvinet, E., Grossmann, E., Rougier, C., Dahmane, M., Meunier, J.: Left-luggage detection using homographies and simple heuristics. In: Proceedings of IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS), New York, pp. 51–58 (2006).

Keywords
Object Detection, Video Surveillance, Video Event Detection, Region of Interest (ROI).