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A Real-Time AdaBoost Cascade Face Tracker Based on Likelihood Map and Optical Flow
Halmstad University, School of Information Technology.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-1400-346X
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-4929-1262
2017 (English)In: IET Biometrics, ISSN 2047-4938, E-ISSN 2047-4946, Vol. 6, no 6, p. 468-477Article in journal (Refereed) Published
Abstract [en]

We 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. We also evaluate two recently published face detectors based on Convolutional Networks and Deformable Part Models, with our algorithm showing a comparable accuracy at a fraction of the computation time.

Place, publisher, year, edition, pages
Stevenage: The Institution of Engineering and Technology , 2017. Vol. 6, no 6, p. 468-477
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:hh:diva-33836DOI: 10.1049/iet-bmt.2016.0202ISI: 000415218200012OAI: oai:DiVA.org:hh-33836DiVA, id: diva2:1094875
Projects
SIDUS-AIR
Funder
Swedish Research Council, 2012-4313Knowledge Foundation, CAISRKnowledge Foundation, SIDUS-AIRAvailable from: 2017-05-11 Created: 2017-05-11 Last updated: 2017-11-29Bibliographically approved

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Alonso-Fernandez, FernandoKarlsson, StefanBigun, Josef
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