1. What have the authors contributed in "Application for video analysis based on machine learning and computer vision algorithms" ?
An application for video data analysis based on computer vision methods is presented.
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Application for video analysis based on machine learning and computer vision algorithms
Vladimir Pavlov,Vladimir Khryashchev,Evgeny Pavlov,Lev Shmaglit +3 more
- 01 Nov 2013
- Vol. 232, Iss: 14, pp 90-100
TL;DR: An application for video data analysis based on computer vision methods is presented, which consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and statistics analysis.
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Abstract: An application for video data analysis based on computer vision methods is presented. The proposed system consists of five consecutive stages: face detection, face tracking, gender recognition, age classification and statistics analysis. AdaBoost classifier is utilized for face detection. A modification of Lucas and Kanade algorithm is introduced on the stage of tracking. Novel gender and age classifiers based on adaptive features and support vector machines are proposed. All the stages are united into a single system of audience analysis. The proposed software complex can find its applications in different areas, from digital signage and video surveillance to automatic systems of accident prevention and intelligent human-computer interfaces.
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Figures
Fig. 7. Detected fragments from the proposed image database. 
Fig. 4. Test frames from videosequence 
Fig. 1. A block diagram of the proposed application for video analysis 
Fig. 5. Tracking algorithms testing results 
Fig. 6. The scheme of the proposed gender classification algorithm 
Fig. 8. ROC-curves of tested gender recognition algorithms
Citations
A comprehensive review of recent advances on deep vision systems
TL;DR: A recent survey of the state-of-the-art DL platforms and architectures used for deep vision systems describes the architecture of various DL models such as AutoEncoders, deep Boltzmann machines, convolution neural networks, recurrent neural networks and deep residual learning.
98
Implementation of machine learning for gender detection using CNN on raspberry Pi platform
Mitulgiri H. Gauswami,Kiran Trivedi +1 more
- 01 Jan 2018
TL;DR: The proposed system describes gender detection based on Computer Vision and Machine Learning Approach using Convolutional Neural Network which is used to extract various facial feature through the use of convolution neural network.
22
SecureHouse: A home security system based on smartphone sensors
Michael A. Mahler,Qinghua Li,Ang Li +2 more
- 13 Mar 2017
TL;DR: This paper designs machine learning and threshold-based methods to detect door opening events based on accelerometer and magnetometer data, and builds a prototype home security system that can detect door openings and notify the homeowner via email, SMS and phone calls upon break-in detection.
18
Age estimation from face images: challenging problem for audience measurement systems
Vladimir Khryashchev,Alexander Ganin,Olga Stepanova,Anton Lebedev +3 more
- 01 Mar 2014
TL;DR: A novel algorithm consisting of two stages: adaptive feature extraction based on local binary patterns and support vector machine classification is proposed, which allows a comparison of the ability of machines and humans in age estimation.
References
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An iterative image registration technique with an application to stereo vision
Bruce D. Lucas,Takeo Kanade +1 more
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An Iterative ImageRegistration Technique with an Application to Stereo Vision
B. D. Lucas
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TL;DR: This work presents a new image registration technique that makes use of the spatial intensity gradient of the images to find a good match using a type of Newton-Raphson iteration, and can be generalized to handle rotation, scaling and shearing.
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Richard Szeliski
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