[1] Liu Z D, Dong L Q, Zhao Y J et al. Adaptive model tracking algorithm for fast-moving targets in video[J]. Acta Optica Sinica, 41, 1815001(2021).

[2] Zou Z Y, Gai S Y, Da F P et al. Occluded pedestrian detection algorithm based on attention mechanism[J]. Acta Optica Sinica, 41, 1515001(2021).

[3] Sawada J, Kusumoto K, Munakata T et al. A mobile robot for inspection of power transmission lines[J]. IEEE Power Engineering Review, 11, 57(1991).

[4] Zhang W Q, Wang Z Q, Zhang L. Human action recognition combining sequential dynamic images and two-stream convolutional network[J]. Laser & Optoelectronics Progress, 58, 0210007(2021).

[5] Liu H, Tu J H, Liu M Y. Two-stream 3D convolutional neural network for skeleton-based action recognition[EB/OL]. https://arxiv.org/abs/1705.08106

[6] Zhu M K, Lu X L. Human action recognition algorithm based on Bi-LSTM-Attention model[J]. Laser & Optoelectronics Progress, 56, 151503(2019).

[7] Liu J, Shahroudy A, Xu D et al. Spatio-temporal LSTM with trust gates for 3D human action recognition[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9907, 816-833(2016).

[8] Dong X W, Thanou D, Rabbat M et al. Learning graphs from data: a signal representation perspective[J]. IEEE Signal Processing Magazine, 36, 44-63(2019).

[9] Yan S J, Xiong Y J, Lin D H. Spatial temporal graph convolutional networks for skeleton-based action recognition[EB/OL]. https://arxiv.org/abs/1801.07455

[10] Li M S, Chen S H, Chen X et al. Actional-structural graph convolutional networks for skeleton-based action recognition[C], 3590-3598(2019).

[11] Thakkar K C, Narayanan P J. Part-based graph convolutional network for action recognition[EB/OL]. https://arxiv.org/abs/1809.04983

[12] Shi L, Zhang Y F, Cheng J et al. Two-stream adaptive graph convolutional networks for skeleton-based action recognition[C], 12018-12027(2019).

[13] Cheng K, Zhang Y F, He X Y et al. Skeleton-based action recognition with shift graph convolutional network[C], 180-189(2020).

[14] Henaff M, Bruna J, LeCun Y. Deep convolutional networks on graph structured data[EB/OL]. https://arxiv.org/abs/1506.05163

[15] Hara K, Kataoka H, Satoh Y. Learning spatio-temporal features with 3D residual networks for action recognition[C], 3154-3160(2017).

[16] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C], 2261-2269(2017).

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

[18] Shahroudy A, Liu J, Ng T T et al. NTU RGB D: a large scale dataset for 3D human activity analysis[C], 1010-1019(2016).

[19] Zhang P F, Lan C L, Xing J L et al. View adaptive recurrent neural networks for high performance human action recognition from skeleton data[C], 2136-2145(2017).

[20] Li C, Zhong Q Y, Xie D et al. Co-occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation[C], 786-792(2018).