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Face Tracking Using Optical Flow: Real-Time Optical Flow Enhanced AdaBoost Cascade Face Tracker
Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE). Salzburg University of Applied Sciences.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This master thesis deals with real-time algorithms and techniques for face detection and facetracking in videos. A new approach is presented where optical flow information is incorporatedinto the Viola-Jones face detection algorithm, allowing the algorithm to update the expectedposition of detected faces in the next frame. This continuity between video frames is not exploitedby the original algorithm from Viola and Jones, in which face detection is static asinformation from previous frames is not considered.In contrast to the Viola-Jones face detector and also to the Kanade-Lucas-Tomasi tracker, theproposed face tracker preserves information about near-positives.In general terms the developed algorithm builds a likelihood map from results of the Viola-Jones algorithm, then computes the optical flow between two consecutive frames and finallyinterpolates the likelihood map in the next frame by the computed flow map. Faces get extractedfrom the likelihood map using image segmentation techniques. Compared to the Viola-Jonesalgorithm an increase in stability as well as an improvement of the detection rate is achieved.Firstly, the real-time face detection algorithm from Viola and Jones is discussed. Secondly theauthor presents methods which are suitable for tracking faces. The theoretical overview leadsto the description of the proposed face tracking algorithm. Both principle and implementationare discussed in detail. The software is written in C++ using the Open Computer Vision Libraryas well as the Matlab MEX interface.The resulting face tracker was tested on the Boston Head Tracking Database for which groundtruth information is available. The proposed face tracking algorithm outperforms the Viola-Jones face detector in terms of average detection rate and temporal consistency.

Place, publisher, year, edition, pages
2014. , 101 p.
Keyword [en]
Face, Tracking, Likelihood Map, Optical Flow, Viola-Jones, AdaBoost Cascade Classifier, OpenCV, C++, MEX, Boston Head Tracking Database
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-26428Local ID: IDE1405OAI: oai:DiVA.org:hh-26428DiVA: diva2:746004
Subject / course
Computer science and engineering
Supervisors
Examiners
Available from: 2014-09-15 Created: 2014-09-11 Last updated: 2014-09-15Bibliographically approved

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