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http://dx.doi.org/10.6109/jkiice.2020.24.10.1262

Realtime Theft Detection of Registered and Unregistered Objects in Surveillance Video  

Park, Hyeseung (School of Computer Science and Engineering, Korea University of Technology and Education)
Park, Seungchul (School of Computer Science and Engineering, Korea University of Technology and Education)
Joo, Youngbok (School of Computer Science and Engineering, Korea University of Technology and Education)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.10, 2020 , pp. 1262-1270 More about this Journal
Abstract
Recently, the smart video surveillance research, which has been receiving increasing attention, has mainly focused on the intruder detection and tracking, and abandoned object detection. On the other hand, research on real-time detection of stolen objects is relatively insufficient compared to its importance. Considering various smart surveillance video application environments, this paper presents two different types of stolen object detection algorithms. We first propose an algorithm that detects theft of statically and dynamically registered surveillance objects using a dual background subtraction model. In addition, we propose another algorithm that detects theft of general surveillance objects by applying the dual background subtraction model and Mask R-CNN-based object segmentation technology. The former algorithm can provide economical theft detection service for pre-registered surveillance objects in low computational power environments, and the latter algorithm can be applied to the theft detection of a wider range of general surveillance objects in environments capable of providing sufficient computational power.
Keywords
Stolen object detection; Smart video surveillance; Dual background subtraction model; Mask R-CNN;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 H. E. Park, S. C. Park, and Y. B. Joo, "Robust Realtime Detection of Abandoned Objects using a Dual Background Model," KSII Transaction on Internet and Information Systems, vol. 14, no. 2, pp. 771-788, Feb. 2020.   DOI
2 H. E. Park, S. C. Park, and Y. B. Joo, "Detection of Abandoned and Stolen Objects Based on Dual Background Model and Mask R-CNN," IEEE Access, vol. 8, pp. 80010-80019, Feb. 2020.   DOI
3 K. He, G. Gkioxari, P. Dollar, and R. Girshick, "Mask R-CNN," 2017 IEEE International Conference on Computer Vision (ICCV), Venice, pp. 2980-2988, Oct. 2017.
4 F. Porikli, Y. Ivanov, and T. Haga, "Robust Abandoned Object detection using Dual Foregrounds," EURASIP Journal on Advances in Signal Processing, vol. 2008, pp. 1-11, Oct. 2007.
5 K. Lin, S. Chen, C. Chen, D. Lin, and Y. Hung, "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, pp. 1359-1370, Jul. 2015.   DOI
6 J. Connell, A. W. Senior, A. Hampapur, Y. -. Tian, L. Brown, and S. Pankanti, "Detection and Tracking in the IBM PeopleVision System," 2004 IEEE International Conference on Multimedia and Expo (ICME), Taipei, pp. 1403-1406, 2004.
7 E. Luna, J. C. Miguel, D. Ortego, and J. M. Martinez, "Abandoned Object Detection in Video-Surveillance: Survey and Comparison," Sensors, vol. 18, no. 12 pp. 1-32, Dec. 2018.   DOI
8 P. L. Venetianer, Z. Zhang, W. Yin, and A. J. Lipton, "Stationary Target Detection using the Objectvideo Surveillance System," 2007 IEEE Conference on Advanced Video and Signal Based Surveillance, London, pp. 242-247, Sep. 2007.
9 Y. Tian, R. S. Feris, H. Liu, A. Hampapur, and M. Sun, "Robust Detection of Abandoned and Removed Objects in Complex Surveillance Videos," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 41, no. 5, pp. 565-576, Sep. 2011.   DOI
10 Z. Zivkovic and F. van der Heijen, "Efficient Adaptive Density Estimation per Image Pixel for the Task of Background Subtraction," Pattern Recognition Letter, vol. 27, no. 7, pp. 773-780, May. 2006.   DOI
11 F. Massa and R. Girshick. (2019). Maskrcnn-Benchmark : Fast, Modular Reference Implementation of Instance Segmentation and Object Detection Algorithms in PyTorch [Internet]. Available: http://github.com/facebookresearch/detectron2
12 H. E. Park, S. C. Park, and Y. B. Joo, "Robust Detection of Abandoned Object for Smart Video Surveillance in Illumination Changes," Sensors, vol. 19, no. 23, pp. 1-17, Nov. 2019.   DOI
13 H. S. Park, J. H. Park, H. G. Kim, S. Q. Lee, and K. H. Park, "Hybrid Sensor Network-Based Indoor Surveillance System for Intrusion Detection," Symmetry 2018, vol. 10, no. 181, pp. 1-17, 2018.
14 L. Jiao, F. Zhang, F. Liu, S. Yang, L. Li, Z. Feng, and R. Qu, "A Survey of Deep Learning-based Object Detection," IEEE Access, vol. 7, pp. 128837-128868, Sep. 2019.   DOI