Design & Implementation of a Gesture Recognition System Isaac Gerg B.S. Computer Engineering The Pennsylvania State University.

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Presentation transcript:

Design & Implementation of a Gesture Recognition System Isaac Gerg B.S. Computer Engineering The Pennsylvania State University

Necessity Kiosks Vehicle Control Video Gaming Large Screen OS Control Novelty

Types of Gestures Static Gestures Dynamic Gestures

MTrack Software Characteristics Runs in Windows COTS Hardware Support Utilizes DirectX Classifier Characteristics Recognize four fundamental gestures plus variations for a total of 9 actions.

System Architecture 5 Stages

System Architecture Stages (in order or processing) 1.RGB to HSV Colorspace conversion. 2.Image Thresholding (pdf) 3.CAMSHIFT 4.Microstate Assignment 5.Action Engine Macrostate Assignment Win 32 API

Thresholding

Dealing with Noise Mathematical Morphology Operations

Discriminant Hu Invariant Moments Scale, Rotation, and Translation Invariant

Classification

The need for a Distance Metric.

Classifier The Mahalanobis Distance Minimum Distance Classifier x t = feature vector at time t of unknown class. m = mean vector of samples. S = covariance matrix of samples.

Micro/Macrostates Statistical physics paradigm Last chance to correct before taking action Provides contextual analysis Implemented using order statistics

MTrack in Action

Tracker Settings

The Future Video Filtering (Wiener Filtering, Kalman Filtering) Morphological Filtering Trainable Data Sets Macrostate Improvement

References J. Flusser and T. Suk, "Affine Moment Invariants: A New Tool for Character Recognition, " Pattern Recognition Letters, Vol. 15, pp , Apr Bradski, G. R., “Computer Vision Face Tracking For Use In A Perceptual User Interface.” Intel Technology Journal, 1998(2).