Review of Abandoned Object Detection Techniques  
  Authors : Komal Sunil Patil; Girish A. Kulkarni

 

In recent years due to various kinds of social activities such as theft, bomb attack and other terrorist attack preventive security measures at public places has gained lot of importance. Abandoned Object detection is most crucial task in visual surveillance system. Many Public or open areas are facilitated with cameras at the multiple angles to monitor the security of that area for keeping citizens safe. This is known as the surveillance system. In this paper a new algorithm is proposed for object tracking in video, which is based on image segmentation. With the image segmentation all objects in video can be detected whether they are moving or not by using segmentation results of successive frames. This approach definitely provides security and detects the moving object in a real time video sequence and live video streaming. Attempt of this paper is to study all the different techniques available for abandoned detection and to find out the research possibilities available in this area.

 

Published In : IJCAT Journal Volume 5, Issue 1

Date of Publication : January 2018

Pages : 12-17

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Publication Link :Review of Abandoned Object Detection Techniques

 

 

 

Komal Sunil Patil : ME 2nd Year, North Maharashtra University, SSGB Engineering & Technology, Bhusawal, Maharashtra.

Girish A. Kulkarni : HOD of E&Tc, North Maharashtra University, SSGB Engineering & Technology, Bhusawal, Maharashtra.

 

 

 

 

 

 

 

Abandoned luggage detection, abandoned object detection, object detection and tracking, video surveillance, left baggage detection, background subtraction.

1) There are different techniques that can be available for abandoned object detection out of Background subtraction, Blob Detection and Tracking, Morphological Processing which are useful. 2) There are different techniques of abandoned object detection by using short term and long term parameters. 3) Problem formulation can be done by identifying various methods\techniques such as Background Modeling and Subtraction, Foreground Analysis, Blob Extraction etc. 4) The temporal consistency model is described by a very simple FSM. It exploits the temporal transition pattern generated by short-term and long-term background models, which can accurately identify static foreground objects. 5) The back-tracing algorithm tracks the luggage owner by using spatial-temporal windows to efficiently verify left-luggage events.

 

 

 

 

 

 

 

 

 

[1] Kevin Lin, Shen-Chi Chen, Chu-Song Chen, Daw- Tung Lin, and Yi-Ping Hung, Senior Member, IEEE,” Abandoned Object Detection via Temporal Consistency Modeling and Back-Tracing Verification for Visual Surveillance” , Ieee Transactions On Information Forensics And Security, Vol. 10, No. 7, July 2015. [2] Rajesh Kumar Tripathi, Anand Singh Jalal,”A Framework for Abandoned Object Detection from Video Surveillance”,Dept. of Computer Engineering & Applications GLA University, Mathura Mathura, India. [3] A. Singh, S. Sawan, M. Hanmandlu,”An abandoned object detection system based on dual background segmentation”, Department of Electrical Engineering I.I.T. Delhi Delhi, India,2009 Advanced Video and Signal Based Surveillance. [4] 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. [5] Q. Fan, P. Gabbur, and S. Pankanti, ?Relative attributes for largescale abandoned object detection,? in Proc. IEEE ICCV, Dec. 2013, pp. 2736–2743. [6] 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. [7] 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. [8] Q. Fan, P. Gabbur, and S. Pankanti, “Relative attributes for largescale abandoned object detection,” in Proc. IEEE ICCV, Dec. 2013, pp. 2736–2743. [9] 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.