Laser & Optoelectronics Progress, Volume. 57, Issue 18, 181006(2020)
Interactive Behavior Recognition Based on Sparse Coding Feature Fusion
Fig. 1. Pixel gray value of 3×3 window
Fig. 2. Neighborhood of circular symmetry. (a) (1,8); (b) (1.5,12); (c) (2,16)
Fig. 3. Processing results of the two modes. (a) Gray image; (b) original LBP operator; (c) improved LBP operator
Fig. 4. Neighborhood of pixel point
Fig. 5. Adjacent pixel
Fig. 6. Edges extracted by different thresholds. (a) Depth image; (b) automatic threshold; (c) threshold range is [0.32,0.8]; (d) threshold range is [0.08,0.2]
Fig. 7. Specific steps of the ScSPM model
Fig. 8. Flow chart of our algorithm
Fig. 9. Recognition results of the CAD-60 dataset
Fig. 10. Recognition results of the MSR Action Pairs dataset
Fig. 11. Recognition results of the SBU Kinect interaction dataset
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Jianjun Li, Yue Sun, Baohua Zhang. Interactive Behavior Recognition Based on Sparse Coding Feature Fusion[J]. Laser & Optoelectronics Progress, 2020, 57(18): 181006
Category: Image Processing
Received: Dec. 11, 2019
Accepted: Feb. 11, 2020
Published Online: Sep. 2, 2020
The Author Email: Sun Yue (sunyueya0526@163.com)