Continuous pattern detection and recognition in stream - a benchmark for online gesture recognition. (2017)

• Record Type: Journal Article
• Authors: Ghouaiel, Nehla; Marteau, Pierre-FranÁ§ois; Dupont, Marc
• Is Part Of: International journal of applied pattern recognition. Volume 4:Number 2(2017)
• Page Start: 146
• Page End: 160
• Publication Date: 2017
• Subjects: stream processing -- pattern detection -- pattern recognition -- gesture recognition -- motion capture -- MOCAP; Pattern perception -- Periodicals; Pattern recognition systems -- Periodicals; 006.405
• Languages: English
• ISSNs: 2049-887X

Abstract

There exist few benchmarks for assessing pattern detection and recognition in streams in general and for gesture processing in particular. We propose a dedicated benchmark based on the construction of isolated gestures (patterns) and gesture sequences datasets. This benchmark is associated with a general assessment methodology for streaming processing which first consists in labelling the stream according to some heuristics (that can be optimised on training data) and then aligning the ground truth labelling with the predicted one. Six pattern recognition models (including DTW, KDTW, HMM, HCRF and SVM) have been accordingly evaluated using this benchmark. It turns out that the regularised kernelised version of DTW measure (KDTW) associated to a SVM is quite efficient, comparatively to the other models, for detecting and recognising continuous gestures in streams.