基于自学习稀疏表示的动态手势识别方法

doi:10.3969/j.issn.1000-436X.2013.06.016

Abstract

Abstract:

An approach of robust accelerometer-based hand gesture recognition based on self-learning sparse representa-tion was proposed. This method operated directly on the original acceleration signals by sparse representation without feature extraction and used the class-specific dictionary learning for sparse modeling to reduce the computing cost and time of recognition. The proposed system can easily add a novel gesture category as well as remove existing ones. Ex-periments on real-world database of 18 hand gestures validate the availability of the proposed algorithm.

Key words: hand gesture recognition, sparse representation, dictionary learning, accelerometer

Ling XIAO,Ren-fa LI,Fan-zai ZENG,Wei-lan QU. Gesture recognition approach based on learning sparse representation[J]. Journal on Communications, 2013, 34(6): 128-135.

Figures/Tables 8

References 17

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L-SRC	20	K
L-SRC	20	40	60
T=10	99.2	99.3	99.1
T=20	99.2	99.2	99.2
T=40	—	99.1	99.2

分类方法	识别率/%	所用时间/ms
SRC	99.4	280
L-SRC(K=60)	99.3	145
L-SRC(K=40)	99.3	106
L-SRC(K=20)	99.2	70

识别方法	手势种类	识别率/%
识别方法	手势种类	个体相关	个体无关
L-SRC	8～18	100～98.7	99.6～99.3
NN	8～18	99.3～97.7	91.2～88.9
文献[8]	8～18	100～99.8	96.8～94.6
HMM^[6]	8～18	99.9～99.5	75.9～71.5
Uwave^[9]	8	98.6	—

Gesture recognition approach based on learning sparse representation

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类别	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	识别率/%
1	175	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100
2	2	173	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	99.8
3	0	0	175	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100
4	1	0	0	175	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100
5	0	0	0	0	175	0	0	0	0	0	0	0	0	0	1	2	0	0	98.3
6	0	0	0	1	0	168	0	0	0	0	0	0	0	0	0	1	0	5	96.0
7	0	0	0	3	0	0	172	0	0	0	0	0	0	0	0	0	0	0	98.3
8	0	0	0	0	0	0	0	175	0	0	1	0	0	0	0	0	0	0	100
9	0	0	0	0	0	0	0	0	175	0	0	0	0	0	0	0	0	0	100
10	0	0	0	0	0	0	0	0	0	175	0	0	0	0	0	0	0	0	100
11	0	0	0	0	0	1	0	0	0	1	174	0	0	0	0	0	0	0	99.4
12	0	0	0	0	0	0	0	0	1	0	0	174	0	0	0	0	0	0	99.4
13	0	0	0	0	0	0	0	0	0	0	0	0	173	0	0	0	2	0	98.9
14	0	0	0	0	0	0	0	0	0	0	0	0	0	175	0	0	0	0	100
15	0	0	0	0	0	0	0	0	0	0	0	0	0	0	175	0	0	0	100
16	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	175	0	0	100
17	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	175	0	100
18	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	174	99.4

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