NDLI: Real-time AdaBoost cascade face tracker based on likelihood map and optical flow

Content Provider	IET Digital Library
Author	Ranftl, Andreas Fernandez, Fernando Alonso Karlsson, Stefan Bigun, Josef
Abstract	The authors present a novel face tracking approach where optical flow information is incorporated into a modified version of the Viola–Jones detection algorithm. In the original algorithm, detection is static, as information from previous frames is not considered; in addition, candidate windows have to pass all stages of the classification cascade, otherwise they are discarded as containing no face. In contrast, the proposed tracker preserves information about the number of classification stages passed by each window. Such information is used to build a likelihood map, which represents the probability of having a face located at that position. Tracking capabilities are provided by extrapolating the position of the likelihood map to the next frame by optical flow computation. The proposed algorithm works in real time on a standard laptop. The system is verified on the Boston Head Tracking Database, showing that the proposed algorithm outperforms the standard Viola–Jones detector in terms of detection rate and stability of the output bounding box, as well as including the capability to deal with occlusions. The authors also evaluate two recently published face detectors based on convolutional networks and deformable part models with their algorithm showing a comparable accuracy at a fraction of the computation time.
Starting Page	468
Ending Page	477
Page Count	10
ISSN	20474938
Volume Number	6
e-ISSN	20474946
Issue Number	Issue 6, Nov (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/6/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2016.0202
Journal	IET Biometrics
Publisher Date	2017-05-15
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Boston Head Tracking Database Computer Vision And Image Processing Technique Convolutional Network Deformable Part Model Face Recognition Image Recognition Image Sequence Interpolation Interpolation And Function Approximation Knowledge Engineering Technique Learning in AI Likelihood Map Interpolation Numerical Analysis Object Detection Object Tracking Optical Flow Probability Real-time AdaBoost Cascade Face Tracker Viola-Jones Detection Algorithm
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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