Human Behavior Recognition for Embedded System

Nana Fu; Daming Liu; Hengbo Zhang; Xuandong Li

doi:10.3788/LOP202259.2215001

[1] Guo F Z, Kong J, Jiang M. Action recognition based on adaptive fusion of RGB and skeleton features[J]. Laser & Optoelectronics Progress, 57, 201506(2020).

[2] Liu S L, Gu J H, Wang H Y et al. Human behavior recognition based on associative partition and ST-GCN[J]. Computer Engineering and Applications, 57, 168-175(2021).

[3] Ren G Y, Lü X Q, Li Y H. Multi-feature fusion real-time action recognition based on 2D to 3D skeleton[J]. Laser & Optoelectronics Progress, 58, 2410010(2021).

[4] Li M H, Xu H J, Shi L X et al. Multi-person activity recognition based on bone keypoints detection[J]. Computer Science, 48, 138-143(2021).

[5] Tang Q C. NVIDIA releases the world’s smallest edge AI supercomputing module[J]. China Scitechnology Business, 108(2019).

[6] Su C, Wang G Z. Research on student behavior recognition based on improved OpenPose[J]. Application Research of Computers, 38, 3183-3188(2021).

[7] Fang H S, Xie S Q, Tai Y W et al. RMPE: regional multi-person pose estimation[C], 2353-2362(2017).

[8] Cao Z, Simon T, Wei S H et al. Realtime multi-person 2D pose estimation using part affinity fields[C], 1302-1310(2017).

[9] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. https://arxiv.org/abs/1409.1556

[10] Howard A G, Zhu M L, Chen B et al. MobileNets: efficient convolutional neural networks for mobile vision applications[J]. International Journal of Computer Vision, 5, 122-131(2017).

[11] Hu J L, Shi Y P, Xie S Y et al. Improved Mobile Net face recognition system based on Jetson nano[J]. Transducer and Microsystem Technologies, 40, 102-105(2021).

[12] Su H S, Liu T T, Liu G H et al. Algorithm for student behavior detection based on neural network[J]. Laser & Optoelectronics Progress, 57, 221016(2020).

[13] Catal C, Tufekci S, Pirmit E et al. On the use of ensemble of classifiers for accelerometer-based activity recognition[J]. Applied Soft Computing, 37, 1018-1022(2015).

[14] Jia X Y, Wang E H, Wu J Y. Real-time human behavior recognition based on Android platform[J]. Computer Engineering and Applications, 54, 164-167, 175(2018).

[15] Liu Y, Jiang H Y, Wang S L et al. Real-time human activity pattern recognition based on time domain features of acceleration[J]. Journal of Shanghai Jiao Tong University, 49, 169-172(2015).