Authors I.V. Loshkarev, Y.M. Demyanenko
Month, Year 04, 2016 @en
Index UDC 004.93
Abstract Contactless monitoring systems are one of the most prominent areas of research in video monitoring and computer vision. The need for monitoring occurs in situations of monotonous activities, when errors can lead to catastrophic consequences. The paper touches upon the problem of determining the position of a person in the video in order to extract characteristics that define human condition. A review of modern methods of search and tracking area of the face in the photo and video is given. It is shown that most of the methods have drawbacks that do not allow their application in real-time. Paper introduces an approach to solving the problem in real time, based on the processing of each frame of video using the generalized Hough transform. This transform is based upon an edge data which allows it to avoid some of the disadvantages of reviewed methods. Iterative application of transformation and gradual refinement of template allows to save the position of the face between frames and not to lose it when face changes angle. The experimental results show that the combined approach in 60% of cases successfully detects an object lost by the standard method of Viola-Jones.

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Keywords Computer vision; video tracking; Hough transform; contactless monitoring
References 1. Kakumanu P., Makrogiannis S., Bourbakis N. A Survey of Skin-Color Modeling and Detection Methods, Pattern Recognition, 2007, Vol. 40, No. 3, pp. 25-60.
2. Stern H. and Efros B. Adaptive Color Space Switching For Tracking Under Varying Illumination, International Journal of Image and Vision Computing, 2005, Vol. 23, pp. 34-57.
3. Raja Y., McKenna S. and Gong S. Color model selection and adaptation in dynamic scenes, Proceedings of the European Conference on Computer Vision, 1998, Vol. 1406, pp. 44-72.
4. Maurer T. and Malsburg C. Tracking and Learning Graphs And Pose on Image Sequences of Faces, Proceedings of the International Conference on Automatic Face and Gesture Recognition, 1996, pp. 176-181.
5. McKenna S., Gong S., Wьrtz R., Tanner J. and Banin D. Tracking Facial Feature Points with Gabor Wavelets and Shape Models, International Conference on Audio and Video-based Biometric Person Authentication, 1997, Vol. 1206, pp. 35-42.
6. Birchfield S. An Elliptical Head Tracker, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems & Computers, 1997, Vol. 2, pp. 1710-1714.
7. Pardаs M. and Sayrol E. A New Approach to Tracking with Active Contours, International Conference on Image Processing, 2000, Vol. 2, pp. 259-262.
8. Bing X., Wei Y. and Charoensak C. Face Contour Tracking in Video using Active Contour Model, International Conference on Image Processing, October 2004, Vol. 2, pp. 1021-1024.
9. Skopchanov M.V. Uluchshennyy algoritm avtomaticheskogo slezheniya za litsom, obnavlyayushchiy izobrazhenie tseli v real'nom vremeni [Improved algorithm for automatic face tracking, obnavlja reinforces the image of the target in real time], Visnik SevNTU [Bulletin of SevNTU], 2012, No. 125, pp. 70-74.
10. Mattoccia S., Tombari F., Di Stefano L. Fast full-search equivalent template matching by enhanced bounded correlation, IEEE Transactions on Image Processing, 2008, Vol. 17, pp. 528-538.
11. Baker S. and Matthews I. Lucas-kanade 20 years on: A Unifying Framework, International Journal of Computer Vision, March 2004, Vol. 56, No. 3, pp. 221-255.
12. Hager G.D. and Belhumeur P.N. Efficient Region Tracking with Parametric Models of Geometry and Illumination, IEEE Transactions Pattern Analysis and Machine Intelligence, October 1998, Vol. 20, No. 10, pp. 1025-1039.
13. Bichsel M. and Pentland A.P. Human Face Recognition and the Face Image Set’s Topology, CVGIP: Image Understanding, 1994, Vol. 59, pp. 254-261.
14. Viola P. and Jones M.J. Robust real-time face detection, International Journal of Computer Vision, 2004, Vol. 57, No. 2, pp. 117-136.
15. Li S.Z. and Zhang Z.Q. Floatboost Learning and Statistical Face Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, September 2004, Vol. 26, No. 9, pp. 1112-1123.
16. Cootes T., Wheeler G., Walker K. and Taylor C. View-Based Active Appearance Models, International Journal of Image and Vision Computing, September 2002, Vol. 20, pp. 657-664.
17. Sung J.-W. and Kim D. A Background Robust Face Tracking using Active Contour Technique Combined Active Appearance Model, International Conference on Biometrics, 2005, Vol. 3832, pp. 159-165.
18. Sung J.-W. and Kim D. Large Motion Object Tracking using Active Contour Combined Active Appearance Model, International Conference on Computer Vision Systems, January 2006, pp. 31-48.
19. Leavers V.F. Which Hough transform?, Computer Vision Graphics and Image Understanding:Image Processing, 1993, Vol. 58, No. 2, pp. 50-64.
20. Ballard D.H. Generalizing the Hough Transform to Detect Arbitrary Shapes, Pattern Recognition, 1981, Vol. 13, No. 2, pp. 111-122.

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