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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

A New Approach for Suspect Detection in Video Surveillance

verfasst von : Manjeet Singh, Ravi Sahran

Erschienen in: Information and Communication Technology for Sustainable Development

Verlag: Springer Singapore

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Abstract

Face recognition is one of the most relevant applications of image analysis. Humans have very good face identification ability but not enough to deal with lots of faces. But computers have lots of memory and processing power to work with high speed. Our problem focused on detection of face from a video frame, extraction of the face, and to calculate the eigenface after normalizing the face image to match with the database of eigenfaces for the verification or identification propose. Here we are taking Vola johns algorithm into consideration for the face detection and eigenface algorithm for matching face. Face matching operation must be fast enough in video surveillance. We proposed these two methods in video surveillance for detection of suspect in video surveillance.

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Literatur
1.
Ojha, Shipra and Sakhare, Sachin, “Image processing techniques for object tracking in video surveillance-A survey”, Pervasive Computing (ICPC), International Conference, 2015.
2.
Menser, Bernd and Muller, F, “Face detection in color images using principal components analysis”, In Seventh Int’l Conf. on Image Processing and Its Applications, 1999.
3.
Gavrila, Dariu M, “The Visual Analysis of Human Movement: A Survey”, Computer Vision and Image Understanding, Vol. 73, pp. 82–98, 1999.
4.
Jafri, Rabia and Arabnia, Hamid R, “A Survey of Face Recognition Techniques”, journal of Information Processing Systems, ACM computing Survey, Vol. 5, pp. 41–68, 2009.
5.
Zhang, Ruolin and Ding, Jian, “Object tracking and detecting based on adaptive background subtraction”, Procedia Engineering, Vol. 29, pp. 1351–1355, 2012.
6.
Dalal, Navneet and Triggs, Bill, “Histograms of oriented gradients for human detection”, Computer Vision and Pattern Recognition, IEEE Computer Society Conference on, Vol. 1, pp. 886–893, 2005.
7.
Mikolajczyk, Krystian and Schmid, Cordelia and Zisserman, Andrew, “Human Detection Based on a Probabilistic Assembly of Robust Part Detectors”, Computer Vision-ECCV 2004.
8.
Viola, Paul and Jones, Michael J, “Robust real-time face detection”, International journal of computer vision, Vol. 57, pp. 137–154, 2004.
9.
Zhao, Wenyi and Chellappa, Rama and Phillips, P Jonathon and Rosenfeld, Azriel, “Face recognition: A literature survey”, Acm Computing Surveys (CSUR), Vol. 35, pp. 399–458, 2003.
10.
Wu, Jianxin and Hua Zhou, Zhi, “Face recognition with one training image per person”, Pattern Recognition Letters, Vol. 23, pp. 1711–1719, 2002.
11.
Turk, Matthew and Pentland, Alex P and others, “Face recognition using eigenfaces”, Computer Vision and Pattern Recognition, pp. 586–591, 1991.
12.
Brunelli, Roberto and Poggio, Tomaso, “Face recognition: Features versus templates”, IEEE Transactions on Pattern Analysis & Machine Intelligence, 1993.
13.
Cleber Zanchettin, “Face Recognition based on Global and Local Features”, Proceedings of the 29th Annual ACM Symposium on Applied Computing, 2014.
14.
Hennings-Yeomans, Pablo H and Baker, Simon and Kumar, BVKV, “Recognition of low-resolution faces using multiple still images and multiple cameras”, 2nd IEEE International Conference, 2008.
15.
Moghaddam, Baback and Jebara, Tony and Pentland, Alex, “Bayesian face recognition”, Computer Vision and Pattern Recognition, Vol. 33, pp. 1771–1782, 2000.
16.
Zhu, Xiangxin and Ramanan, Deva, “Face Detection, Pose Estimation, and Landmark Localization in the Wild”, Computer Vision and Pattern Recognition (CVPR), IEEE Conference, 2012.
17.
Ogale, Neeti A, “A survey of techniques for human detection from video”, Survey, University of Maryland, 2006.
18.
Chatrath, Jatin and Gupta, Puneet and Ahuja, Puneet and Goel, Ankush and Arora, Shaifali Madan, “Real time human face detection and tracking”, Signal Processing and Integrated Networks, 2014.
Metadaten
Titel
A New Approach for Suspect Detection in Video Surveillance
verfasst von
Manjeet Singh
Ravi Sahran
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Information and Communication Technology for Sustainable Development

Print ISBN: 978-981-10-3919-5

Electronic ISBN: 978-981-10-3920-1

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-10-3920-1_44

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