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Novel Cost Measures For Robust Recognition Of Dynamic Hand Gestures

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Novel Cost Measures For Robust Recognition Of Dynamic Hand Gestures

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Title: Novel Cost Measures For Robust Recognition Of Dynamic Hand Gestures
Author: Kulkarni, Ameya
Abstract: Computer vision aided automatic hand gesture recognition system plays a vital role in real world human computer interaction applications such as sign language recognition, game controls, virtual reality, intelligent home appliances and assistive robotics. In such systems, when input with a video sequence, the challenging task is to locate the gesturing hand (spatial segmentation) and determine when the gesture starts and ends (temporal segmentation). In this thesis, we use a framework which at its principal has a dynamic space time warping (DSTW) algorithm to simultaneously localize gesturing hand, to find an optimal alignment in time domain between query-model sequences and to compute a matching cost (a measure of how well the query sequence matches with the model sequence) for the query-model pair. Within the context of DSTW, the thesis proposes few novel cost measures to improve the performance of the framework for robust recognition of hand gesture with the help of translation and scale invariant feature vectors extracted at each frame of the input video. The performance of the system is evaluated in a real world scene with cluttered background and in presence of multiple moving skin colored distractors in the background.
URI: http://hdl.handle.net/10106/9530
Date: 2012-04-11

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