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Erschienen in:
Practical Matters in Computer Vision Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

3. The Application of Machine Learning Techniques to Real Time Audience Analysis System

verfasst von : Vladimir Khryashchev, Lev Shmaglit, Andrey Shemyakov

Erschienen in: Computer Vision in Control Systems-2

Verlag: Springer International Publishing

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Abstract

An application for video data analysis based on computer vision methods is presented in this chapter. The proposed system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification, and statistics analysis. The AdaBoost classifier is utilized for face detection. A modification of Lucas and Kanade algorithm is introduced on the stage of face tracking. Novel gender and age classifiers based on adaptive features and support vector machines are proposed. More than 90 % accuracy of viewer’s gender recognition is achieved. All stages are united into a single system of audience analysis. The system allows to extract all possible information about depicted people from the input video stream, aggregate and analyze this information in order to measure different statistical parameters. The proposed software solution can find its applications in different areas, from digital signage and video surveillance to the automatic systems of accident prevention and intelligent human-computer interfaces.

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Vorheriges Kapitel Human Action Recognition: Contour-Based and Silhouette-Based Approaches
Nächstes Kapitel Panorama Construction from Multi-view Cameras in Outdoor Scenes
Literatur
1.
Alpaydin E (2010) Introduction to machine learning, 2nd edn. The MIT Press, CambridgeMATH
2.
Sammut C, Webb GI (eds) (2011) Encyclopedia of machine learning. Springer Science + Business Media, LLC, New York
3.
Li SZ, Jain AK (eds) (2005) Handbook of face recognition. Springer, New YorkMATH
4.
Szeliski R (2010) Computer vision: algorithms and applications. Springer, London
5.
Makinen E, Raisamo R (2008) An experimental comparison of gender classification methods. Pattern Recogn Lett 29(10):1544–1556CrossRef
6.
Tamura S, Kawai H, Mitsumoto H (1996) Male/female identification from 8 to 6 very low resolution face images by neural network. Pattern Recogn Lett 29(2):331–335CrossRef
7.
Khryashchev V, Priorov A, Shmaglit AL, Golubev M (2012) Gender recognition via face area analysis. In: World congress on engineering and computer science, pp 645–649
8.
Khryashchev V, Ganin A, Golubev M, Shmaglit L (2013) Audience analysis system on the basis of face detection, tracking and classification techniques. In: International multi-conference of engineers and computer scientists (IMECS 2013), vol 1, pp 446–450
9.
Fu Y, Huang TS (2010) Age synthesis and estimation via faces: a survey. IEEE Trans Pattern Anal Mach Intell 32(11):1955–1976CrossRef
10.
Sung KK, Poggio T (1998) Example-based learning for view-based human face detection. IEEE Trans Pattern Anal Mach Intell 20(1):39–51CrossRef
11.
Maydt J, Lienhart R (2002) Face detection with support vector machines and a very large set of linear features. In: IEEE international conference on multimedia and expo (ICME’2002), pp 309–312
12.
Yang MH, Roth D, Ahuja N (2000) A SNoW-based face detector. In: Advances in neural information processing systems (NIPS’1999) vol 12:855–861
13.
Juell P, Marsh R (1996) A hierarchical neural network for human face detection. Pattern Recogn 29(5):781–787CrossRef
14.
Rowley HA, Baluja S, Kanade T (1998) Neural network-based face detection. IEEE Trans Pattern Anal Mach Intell 20(1):23–38CrossRef
15.
Lin SH, Kung SY, Lin LJ (1997) Face recognition/detection by probabilistic decision-based neural network. IEEE Trans Neural Netw 8(1):114–132CrossRef
16.
Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: International conference on computer vision and pattern recognition, vol 1, pp 511–518
17.
Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):art No 13
18.
Comaniciu D, Ramesh V, Andmeer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–575CrossRef
19.
Shi J, Tomasi C (1994) Good features to track. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 593–600
20.
Tao H, Sawhney H, Kumar R (2002) Object tracking with bayesian estimation of dynamic layer representations. IEEE Trans Pattern Anal Mach Intell 24(1):75–89CrossRef
21.
Sung EC, Youn JL, Sung JL, Kang RP, Jaihie K (2010) A comparative study of local feature extraction for age estimation. In: IEEE international conference on control automation robotics & vision (ICARCV’2010), pp 1280–1284
22.
Thukral P, Mitra K, Chellappa R (2012) A hierarchical approach for human age estimation. In: IEEE international conference on acoustics, speech and signal processing (ICASSP’2012), pp 1529–1532
23.
Guodong G, Guowang M (2010) Human age estimation: what is the influence across race and gender. In: IEEE computer society conference on computer vision and pattern recognition workshops (CVPRW’2010), pp 71–78
24.
Zhen L, Yun F, Huang TS (2010) A robust framework for multiview age estimation. In: IEEE computer society conference on computer vision and pattern recognition workshops (CVPRW’2010), pp 9–16
25.
Guodong G, Xiaolong W (2012) A study on human age estimation under facial expression changes. In: IEEE conference on computer vision and pattern recognition (CVPR’2012), pp 2547–2553
26.
Wang HL, Yau WY, Chua XL, Tan YP (2010) Effects of facial alignment for age estimation. In: IEEE international conference on control automation robotics & vision (ICARCV’2010), pp 644–647
27.
Kriegman D, Yang MH, Ahuja N (2002) Detecting faces in images: a survey. IEEE Trans Pattern Anal Mach Intell 24(1):34–58CrossRef
28.
29.
Zhao W, Chellappa R, Phillips P, Rosenfeld A (2003) Face recognition: A literature survey. ACM Comput Surv (CSUR’2003) 35(4):399–458
30.
31.
Lucas B, Kanade T (1981) An iterative image registration technique with an application to stereo vision. Imaging Understanding Workshop, pp 121–130
32.
Phillips PJ (2000) The FERET evaluation methodology for face recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22(10):1090–1104CrossRef
33.
Burges C (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:121–167CrossRef
34.
Gao H, Davis J (2006) Why direct LDA is not equivalent to LDA. Pattern Recogn Lett 39(5):1002–1006CrossRefMATH
35.
Ricanek K, Tesafaye T (2006) MORPH: a longitudinal image database of normal adult age-progression. In: IEEE 7th international conference on automatic face and gesture recognition, pp 341–345
36.
Metadaten
Titel
The Application of Machine Learning Techniques to Real Time Audience Analysis System
verfasst von
Vladimir Khryashchev
Lev Shmaglit
Andrey Shemyakov
Copyright-Jahr
2015
Buch
Computer Vision in Control Systems-2

Print ISBN: 978-3-319-11429-3

Electronic ISBN: 978-3-319-11430-9

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-11430-9_3

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