Recognizing Action Using Multi-center Subspace Learning-based Spatial-temporal Information Fusion
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摘要: 基于深度序列的人体行为识别, 一般通过提取特征图来提高识别精度, 但这类特征图通常存在时序信息缺失的问题. 针对上述问题, 本文提出了一种新的深度图序列表示方式, 即深度时空图(Depth space time maps, DSTM). DSTM降低了特征图的冗余度, 弥补了时序信息缺失的问题. 本文通过融合空间信息占优的深度运动图(Depth motion maps, DMM) 与时序信息占优的DSTM, 进行高精度的人体行为研究, 并提出了多聚点子空间学习(Multi-center subspace learning, MCSL)的多模态数据融合算法. 该算法为各类数据构建多个投影聚点, 以此增大样本的类间距离, 降低了投影目标区域维度. 本文在MSR-Action3D数据集和UTD-MHAD数据集上进行人体行为识别. 最后实验结果表明, 本文方法相较于现有人体行为识别方法有着较高的识别率.Abstract: Human action recognitions from depth map sequences improve the recognition accuracy by extracting feature maps. A new representation of depth map sequences called depth space time map (DSTM) is proposed in this paper for overcoming the lack of temporal information in the feature maps. DSTM reduces the redundancy of action features. We conduct high-precision human action recognitions by fusing depth motion maps (DMM) and DSTM based on a new multi-modal data fusion algorithm called multi-center subspace learning (MCSL). The algorithm constructs multiple projection centers for each class data to expand the samples inter-class distance and reduce the projection target area dimension. Experiments conducted on MSR-Action3D and UTD-MHAD depth database show the effectiveness of the proposed method.
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表 1 MSR数据库中的人体行为
Table 1 Human actions in MSR
动作 样本数 动作 样本数 高挥手 (A01) 27 双手挥 (A11) 30 水平挥手 (A02) 26 侧边拳击 (A12) 30 锤 (A03) 27 弯曲 (A13) 27 手抓 (A04) 25 向前踢 (A14) 29 打拳 (A05 26 侧踢 (A15) 20 高抛 (A06) 26 慢跑 (A16) 30 画叉 (A07) 27 网球挥拍 (A17) 30 画勾 (A08) 30 发网球 (A18) 30 画圆 (A09) 30 高尔夫挥杆 (A19) 30 拍手 (A10) 30 捡起扔 (A20) 27 
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表 2 UTD数据库中的人体行为
Table 2 Human actions in UTD
动作 样本数 动作 样本数 向左滑动 (B01) 32 挥网球 (B15) 32 向右滑动 (B02) 32 手臂卷曲 (B16) 32 挥手 (B03) 32 网球发球 (B17) 32 鼓掌 (B04) 32 推 (B18) 32 扔 (B05) 32 敲 (B19) 32 双手交叉 (B06) 32 抓 (B20) 32 拍篮球 (B07) 32 捡起扔 (B21) 32 画叉 (B08) 31 慢跑 (B22) 31 画圆 (B09) 32 走 (B23) 32 持续画圆 (B10) 32 坐下 (B24) 32 画三角 (B11) 32 站起来 (B25) 32 打保龄球 (B12) 32 弓步 (B26) 32 冲拳 (B13) 32 蹲 (B27) 32 挥羽毛球 (B14) 32
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表 3 MSR-Action3D 数据分组
Table 3 MSR-Action3D data grouping
AS1 AS2 AS3 A02 A01 A06 A03 A04 A14 A05 A07 A15 A06 A08 A16 A10 A09 A17 A13 A11 A18 A18 A14 A19 A20 A12 A20
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表 4 MSR数据库上不同特征的识别率(%)
Table 4 Different of feature action recognition on MSR (%)
方法 测试 1 测试 2 测试 3 AS1 AS2 AS3 均值 AS1 AS2 AS3 均值 AS1 AS2 AS3 均值 MEI-HOG 69.79 77.63 79.72 75.71 84.00 89.58 93.24 88.94 86.95 86.95 95.45 89.78 MEI-LBP 57.05 56.58 64.19 59.27 66.66 69.79 78.37 71.61 69.56 73.91 77.27 73.58 DSTM-HOG 83.22 71.71 87.83 80.92 94.66 84.37 88.23 89.80 91.30 82.61 95.95 89.95 DSTM-LBP 84.56 71.71 87.83 81.37 88.00 82.29 95.94 88.74 86.96 82.61 95.45 88.34 MHI-HOG 69.79 72.36 70.95 71.03 88.00 84.37 89.19 87.19 95.65 82.60 95.45 91.23 MHI-LBP 51.67 60.52 54.05 55.41 73.33 70.83 78.37 74.18 82.60 65.21 72.72 73.51 DMM-HOG 88.00 87.78 87.16 87.65 94.66 87.78 100.00 94.15 100.00 88.23 95.45 94.56 DMM-LBP 89.52 87.78 93.20 90.17 93.11 85.19 100.00 92.77 94.03 88.98 92.38 91.80
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表 5 UTD数据库上不同特征的识别率(%)
Table 5 Different of feature action recognition on UTD (%)
方法 测试 1 测试 2 测试 3 MEI-HOG 69.51 65.42 68.20 MEI-LBP 45.12 51.97 52.61 DSTM-HOG 71.08 80.28 89.54 DSTM-LBP 68.81 80.97 86.06 MHI-HOG 56.44 66.58 73.14 MHI-LBP 49.82 53.82 57.40 DMM-HOG 78.39 75.40 87.94 DMM-LBP 68.98 74.94 86.75
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表 6 DMM和DSTM对比实验结果(%)
Table 6 Experimental results of DMM and DSTM (%)
方法 D1 D2 DSTM 62.83 81.53 DMM 32.17 63.93
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表 7 DMM和DSTM平均处理时间(s)
Table 7 Average processing time of DMM and DSTM (s)
方法 D1 D2 DSTM 2.1059 3.4376 DMM 5.6014 8.6583
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表 8
$ \mathrm{MSR}\text{-}\mathrm{Action} 3 \mathrm{D}^{1} $ 上的实验结果Table 8 Experimental results on
$ \mathrm{MSR}\text{-}\mathrm{Action} 3 \mathrm{D}^{1} $
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表 9
$\mathrm{MSR}\text{-}\mathrm{Action} 3 \mathrm{D}^{2}$ 上的实验结果Table 9 Experimental results on
$\mathrm{MSR}\text{-}\mathrm{Action} 3 \mathrm{D}^{2}$
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