Abstract
The purpose of this study is to develop a kidnapping event detection scheme for intelligent video surveillance by frame-based classification which is able to assort each frame into a kidnapping or normally accompanying situation. In this study, for generating training data from videos, a semi-automatic video annotation tool named INHA-VAT is used. Also, we developed a frame-based event classifier using Bayesian network model to distinguish the frame of kidnapping situations from one of accompanying ones. When a video has more frames of kidnapping situation than the threshold ratio after two people meet in the video, the proposed scheme detects and notifies the occurrence of kidnapping event. To check the feasibility of the proposed scheme, we also performed the accuracy evaluation against test videos. According to the experiment results, the proposed scheme could detect kidnapping situations appropriately according to the threshold ratio.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kang, S.J., Park, J.E., Lee, K.H.: An Analysis for Effect of Crime Preventive CCTV in Residential Areas through Public Opinion Survey. Journal of the Architectural Institute of Korea (Planning & Design) 25(4), 235–244 (2009)
Duque, D., Santos, H., Cortez, P.: Prediction of Abnormal Behaviors for Intelligent Video Surveillance Systems. In: Computational Intelligence and Data Mining (2007)
Tickner, A.H., Poulton, E.C.: Monitoring up to 16 synthetic television pictures showing a great deal of movement. Ergonomics 16, 381–401 (1973)
Ainsworth, T.: Buyer Beware. Security Oz 19, 18–26 (2002)
Kim, J.S., Kim, H.I., Kim, Y.S.: A Video Annotation System with Automatic Human Detection from Video Surveillance Data. In: Korea Computer Congress, vol. 39(1) (2012)
Nascimento, J.C., Figueiredo, M.A.T., Marques, J.S.: Segmentation and Classification of Human Activities. In: International Workshop on Human Activity Recognition and Modeling, Oxford, UK (2005)
Ayers, D., Shah, M.: Monitoring Human Behavior from Video Taken in and Office En-vironment. Image and Vision Computing 19(12-1), 833–846 (2001)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Leaning Tools and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego (2005)
Elarbi- Boudihir, M., Al-Shalfan, K.A.: Intelligent Video Surveillance System Archi-tecture for Abnormal Activity Detection. In: The International Conference on Informatics and Applications, Kuala Terengganu, Malaysia (2012)
Hansen, D.M., Mortensen, B.K., Duizer, P.T.: Automatic Annotation of Humans in Surveillance Video. In: Fourth Canadian Conference on Computer and Robot Vision, Montreal, Canada (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gwon, RH., Kim, KY., Park, JT., Kim, H., Kim, YS. (2013). A Kidnapping Detection Scheme Using Frame-Based Classification for Intelligent Video Surveillance. In: Ciucci, D., Inuiguchi, M., Yao, Y., Ślęzak, D., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2013. Lecture Notes in Computer Science(), vol 8170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41218-9_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-41218-9_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41217-2
Online ISBN: 978-3-642-41218-9
eBook Packages: Computer ScienceComputer Science (R0)