Laser & Optoelectronics Progress, Volume. 57, Issue 18, 181506(2020)
Human Action Recognition Algorithm Based on Spatio-Temporal Interactive Attention Model
Figures & Tables(12)
Fig. 1. Framework of action recognition network based on spatio-temporal interactive attention model
Fig. 2. Local_Mask feature maps generated from UCF101 dataset. (a) Balance beam; (b) walking with dog
Fig. 5. Training and testing iteration curves of each algorithm on UCF101 dataset.(a) Proposed model; (b) proposed model with OGTAM;(c) proposed model with MGSAM;(d) proposed model with OGTAM+MGSAM
Fig. 6. Visualization results of proposed algorithm on different datasets. (a) UCF101; (b) Penn Action
Table 1. Experimental parameters
View tableTable 1. Experimental parameters
Parameter Value Loss function Categorical_cross entropy Optimizer Adam Learning rate 0.0001 Batch size 18 Epoch 150(Penn Action)/250(UCF101)
Table 2. Effects of optical flow guided temporal attention mechanism on UCF101 datasetunit: %
View tableTable 2. Effects of optical flow guided temporal attention mechanism on UCF101 datasetunit: %
Modality attention RGB TVNet With Without With Without 3D ConvNet 76.58 75.43 82.79 81.71 Bi-LSTM 82.22 80.15 80.36 79.38
Table 3. Effects of mask guided spatial attention mechanism on UCF101 dataset%
View tableTable 3. Effects of mask guided spatial attention mechanism on UCF101 dataset%
Attention With Without RGB 85.44 80.15 TVNet 82.62 81.71 RGB+TVNet 92.80 91.70
Table 4. Comparison of proposed model and other basic models on UCF101 dataset%
View tableTable 4. Comparison of proposed model and other basic models on UCF101 dataset%
Model Accuracy VideoLSTM-two stream[ 15 ]89.2 Two-stream MLDF-3D[ 16 ]91.3 Two-stream HHF[ 17 ]91.2 Proposed model 91.7 Proposed model(with OGTAM) 92.2 Proposed model(with MGSAM) 92.8 Proposed model(with OGTAM+MGSAM) 94.9
Table 5. Comparison of accuracy of different algorithms on UCF101 dataset%
View tableTable 5. Comparison of accuracy of different algorithms on UCF101 dataset%
Model Accuracy IDT+FV[ 18 ]85.9 IDT+HSV[ 19 ]87.9 MIFS[ 20 ]89.1 TSN(two modalities)[ 2 ]94.0 Hidden two-stream[ 21 ]93.1 MLDF-3D[ 16 ]94.4 MS-NET[ 22 ]93.9 Two-stream I3D[ 3 ]98.0 Two-stream FCAN-comp[ 23 ]92.0 VideoLSTM[ 15 ]89.2 JSTA[ 11 ]93.7 RSTAN[ 24 ]94.6 VideoYOLO[ 10 ]90.6 Proposed model 91.7 Proposed model(with OGTAM+MGSAM) 94.9
Table 6. Comparison of accuracy of different algorithms on Penn Action dataset%
View tableTable 6. Comparison of accuracy of different algorithms on Penn Action dataset%
Model Accuracy Good-practice CNN 88.6 JDD[ 25 ]87.4 C3D[ 25 ]86.0 TSN-S+T[ 2 ]93.8 GLTF[ 26 ]86.1 Im2Flow[ 27 ]77.4 Spatial 81.7 Temporal 83.4 Proposed model 89.3 Proposed model(with OGTAM) 90.7 Proposed model(with MGSAM) 90.6 Proposed model(with OGTAM+MGSAM) 91.7
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Na Pan, Min Jiang, Jun Kong. Human Action Recognition Algorithm Based on Spatio-Temporal Interactive Attention Model[J]. Laser & Optoelectronics Progress, 2020, 57(18): 181506
Paper Information
Category: Machine Vision
Received: Dec. 23, 2019
Accepted: Feb. 14, 2020
Published Online: Sep. 2, 2020
The Author Email: Min Jiang (minjiang@jiangnan.edu.cn)
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