Tracking Faces using Active-Appearance-Models on calibrated web cams
نویسندگان
چکیده
......................................................................................................................... 2 1. Tracking in real time.................................................................................................. 4 1.1 Inspiration 1.2 Demands 1.3 Libraries 2. Active Appearance Models ....................................................................................... 6 2.1 Interpretation by Synthesis 2.2 Modelling appearance 2.3 Synthesis of an example 2.4 Approximating a new example 2.5 AAM Search 3. AAM Tracking .......................................................................................................... 10 3.1 Implementation 3.2 Excursion: Building a new model 4. Evaluation of the Tracker........................................................................................ 12 4.1 Stereo Vision 4.2 qualitative verification 5. Discussion ............................................................................................................... 15 5.1 Reached goals 5.2 Other aspects 6. Possibilities for further development .................................................................... 16 6.1 Variable model 6.2 Coupling the models of both cameras 6.3 Combination with other algorithms Literature ...................................................................................................................... 18 Appendix ...................................................................................................................... 19 A.1 How to rebuild everything A.2 How to get aamTracker running
منابع مشابه
Active appearance models with occlusion
Active Appearance Models (AAMs) are generative parametric models that have been successfully used in the past to track faces in video. A variety of video applications are possible, including dynamic head pose and gaze estimation for real-time user interfaces, lip-reading, and expression recognition. To construct an AAM, a number of training images of faces with a mesh of canonical feature point...
متن کاملRobust 3D Face Tracking on Unknown Users with Dynamical Active Models
The Active Appearance Models [1] and the derived Active Models (AM) [4] allow to robustly track the face of a single user that was previously learnt, but works poorly with multiple or unknown users. Our research aims at improving the tracking robustness by learning from video databases. In this paper, we study the relation between the face texture and the parameter gradient matrix, and propose ...
متن کاملFacial Features Tracking using Active Appearance Models
This thesis aims at building a system capable of automatically extracting and parameterizing the position of a face and its features in images acquired from a lowend monocular camera. Such a challenging task is justified by the importance and variety of its possible applications, ranging from face and expression recognition to animation of virtual characters using video depicting real actors. T...
متن کاملEye Typing using Markov and Active Appearance Models
We propose a non-intrusive eye tracking system intended for the use of everyday gaze typing using web cameras. We argue that high precision in gaze tracking is not needed for on-screen typing due to natural language redundancy. This facilitates the use of low-cost video components for advanced multi-modal interactions based on video tracking systems. Robust methods are needed to track the eyes ...
متن کاملA Comparison of Adaptive Appearance Methods for Tracking Faces in Video Surveillance
Face recognition is increasingly employed by public safety organizations in decision support systems for video surveillance, to detect the presence of individuals of interest. In the context of spatiotemporal face recognition, tracking is an important function used to locate, follow and regroup faces of different individuals in a scene. Techniques for face tracking in video surveillance should ...
متن کامل