Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (7): 2052-2057.DOI: 10.11772/j.issn.1001-9081.2021060904
• Artificial intelligence • Previous Articles Next Articles
Bo LIU, Linbo QING, Zhengyong WANG(), Mei LIU, Xue JIANG
Received:
2021-06-03
Revised:
2021-09-11
Accepted:
2021-09-24
Online:
2021-10-18
Published:
2022-07-10
Contact:
Zhengyong WANG
About author:
LIU Bo, born in 1997, M. S. candidate. His research interests include computer vision.Supported by:
通讯作者:
王正勇
作者简介:
刘博(1997—),男,河南许昌人,硕士研究生,CCF会员,主要研究方向:计算机视觉基金资助:
CLC Number:
Bo LIU, Linbo QING, Zhengyong WANG, Mei LIU, Xue JIANG. Group activity recognition based on partitioned attention mechanism and interactive position relationship[J]. Journal of Computer Applications, 2022, 42(7): 2052-2057.
刘博, 卿粼波, 王正勇, 刘美, 姜雪. 基于分块注意力机制和交互位置关系的群组活动识别[J]. 《计算机应用》唯一官方网站, 2022, 42(7): 2052-2057.
Add to citation manager EndNote|Ris|BibTeX
URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021060904
本文方法 |
Tab. 1 Accuracies of different methods on CAD dataset
本文方法 |
Tab. 2 Accuracies of different methods on CAE dataset
类别准确率 | |||||||
---|---|---|---|---|---|---|---|
Crossing | Waiting | Queuing | Walking | Talking | |||
Tab. 3 Accuracy comparison of the proposed method and baseline methods on CAD dataset
类别准确率 | |||||||
---|---|---|---|---|---|---|---|
Crossing | Waiting | Queuing | Walking | Talking | |||
Tab. 4 Accuracy comparison of the proposed method and baseline methods on CAE dataset
1 | TRAN D, BOURDEV L, FERGUS R, et al. Learning spatiotemporal features with 3D convolutional networks[C]// Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2015: 4489-4497. 10.1109/iccv.2015.510 |
2 | WANG L M, LI W, LI W, et al. Appearance-and-relation networks for video classification[C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 1430-1439. 10.1109/cvpr.2018.00155 |
3 | IBRAHIM M S, MURALIDHARAN S, DENG Z W, et al. A hierarchical deep temporal model for group activity recognition[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 1971-1980. 10.1109/cvpr.2016.217 |
4 | CHOI W, SHAHID K, SAVARESE S. What are they doing?: collective activity classification using spatio-temporal relationship among people[C]// Proceedings of the IEEE 12th International Conference on Computer Vision Workshops. Piscataway: IEEE, 2009: 1282-1289. 10.1109/iccvw.2009.5457461 |
5 | BAGAUTDINOV T, ALAHI A, FLEURET F, et al. Social scene understanding: end-to-end multi-person action localization and collective activity recognition[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 3425-3434. 10.1109/cvpr.2017.365 |
6 | WU J C, WANG L M, WANG L, et al. Learning actor relation graphs for group activity recognition[C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 9956-9966. 10.1109/cvpr.2019.01020 |
7 | YAN R, TANG J H, SHU X B, et al. Participation-contributed temporal dynamic model for group activity recognition[C]// Proceedings of the 26th ACM International Conference on Multimedia. New York: ACM, 2018: 1292-1300. 10.1145/3240508.3240572 |
8 | 杨兴明,范楼苗. 基于区域特征融合网络的群组行为识别[J]. 模式识别与人工智能, 2019, 32(12): 1116-1121. |
YANG X M, FAN L M. Group activity recognition based on regional feature fusion network[J]. Pattern Recognition and Artificial Intelligence, 2019, 32(12): 1116-1121. | |
9 | 龚玉婷. 基于注意力机制与深度学习网络的群组行为识别方法研究[D]. 青岛:青岛科技大学, 2019:28-29. |
GONG Y T. Group activity recognition algorithm research based on attention mechanism and deep learning network[D]. Qingdao: Qingdao University of Science and Technology, 2019:28-29. | |
10 | SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 2818-2826. 10.1109/cvpr.2016.308 |
11 | HE K M, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C]// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 2980-2988. 10.1109/iccv.2017.322 |
12 | LU L H, DI H J, LU Y, et al. Spatio-temporal attention mechanisms based model for collective activity recognition[J]. Signal Processing: Image Communication, 2019, 74: 162-174. 10.1016/j.image.2019.02.012 |
13 | KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[EB/OL]. (2017-02-22) [2020-11-16].. |
14 | QI M S, QIN J, LI A N, et al. StagNet: an attentive semantic RNN for group activity recognition[C]// Proceedings of the 2018 European Conference on Computer Vision, LNCS 11214. Cham: Springer, 2018: 104-120. |
15 | CHOI W, SHAHID K, SAVARESE S. Learning context for collective activity recognition[C]// Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2011: 3273-3280. 10.1109/cvpr.2011.5995707 |
16 | LI X, CHUAH M C. SBGAR: semantics based group activity recognition[C]// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 2895-2904. 10.1109/iccv.2017.313 |
17 | SHU T M, TODOROVIC S, ZHU S C. CERN: confidence-energy recurrent network for group activity recognition[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 4255-4263. 10.1109/cvpr.2017.453 |
18 | XU D Z, FU H, WU L F, et al. Group activity recognition by using effective multiple modality relation representation with temporal-spatial attention[J]. IEEE Access, 2020, 8: 65689-65698. 10.1109/access.2020.2979742 |
19 | HU G Y, CUI B, HE Y, et al. Progressive relation learning for group activity recognition[C]// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 977-986. 10.1109/cvpr42600.2020.00106 |
20 | DENG Z W, VAHDAT A, HU H X, et al. Structure inference machines: recurrent neural networks for analyzing relations in group activity recognition[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 4772-4781. 10.1109/cvpr.2016.516 |
21 | LI W B, CHANG M C, LYU S W. Who did what at where and when: simultaneous multi-person tracking and activity recognition[EB/OL]. (2018-07-03) [2020-10-09].. 10.1016/j.cviu.2021.103301 |
[1] | Zefang HAN, Xiong ZHANG, Hong SHANGGUAN, Xinglong HAN, Jing HAN, Gang FENG, Xueying CUI. Artifacts sensing generative adversarial network for low-dose CT denoising [J]. Journal of Computer Applications, 2022, 42(7): 2301-2310. |
[2] | Yayao ZUO, Haoyu CHEN, Zhiran CHEN, Jiawei HONG, Kun CHEN. Named entity recognition method combining multiple semantic features [J]. Journal of Computer Applications, 2022, 42(7): 2001-2008. |
[3] | Haiqi WANG, Zhihai WANG, Liuke LI, Haoran KONG, Qiong WANG, Jianbo XU. Spatial-temporal prediction model of urban short-term traffic flow based on grid division [J]. Journal of Computer Applications, 2022, 42(7): 2274-2280. |
[4] | Cheng HUANG, Qianrui ZHAO. Sensitive information detection method based on attention mechanism-based ELMo [J]. Journal of Computer Applications, 2022, 42(7): 2009-2014. |
[5] | Xiaohan LI, Jun WANG, Huading JIA, Liu XIAO. Stock market volatility prediction method based on graph neural network with multi-attention mechanism [J]. Journal of Computer Applications, 2022, 42(7): 2265-2273. |
[6] | Tingwei QIN, Pengcheng ZHAO, Pinle QIN, Jianchao ZENG, Rui CHAI, Yongqi HUANG. Point cloud registration algorithm based on residual attention mechanism [J]. Journal of Computer Applications, 2022, 42(7): 2184-2191. |
[7] | Wenjun FAN, Shuguang ZHAO, Lizheng GUO. Ship detection algorithm based on improved RetinaNet [J]. Journal of Computer Applications, 2022, 42(7): 2248-2255. |
[8] | Wanjun LIU, Jiaming WANG, Haicheng QU, Libing DONG, Xinyu CAO. Music genre classification algorithm based on attention spectral-spatial feature [J]. Journal of Computer Applications, 2022, 42(7): 2072-2077. |
[9] | Yang ZHANG, Jiangbo HAO. Malicious code detection method based on attention mechanism and residual network [J]. Journal of Computer Applications, 2022, 42(6): 1708-1715. |
[10] | Shan SU, Yang ZHANG, Dongwen ZHANG. Coupling related code smell detection method based on deep learning [J]. Journal of Computer Applications, 2022, 42(6): 1702-1707. |
[11] | Jiafan ZHOU, Yuefeng DU, Baoyan SONG, Xiaoguang LI, Azhu ZHAO, Xujie XIAO. MOOC video recommendation method based on meta-path attention mechanism [J]. Journal of Computer Applications, 2022, 42(6): 1808-1813. |
[12] | Xianfeng YANG, Jiahe ZHAO, Ziqiang LI. Text classification model combining word annotations [J]. Journal of Computer Applications, 2022, 42(5): 1317-1323. |
[13] | Wei REN, Hexiang BAI. Multi-label image classification method based on global and local label relationship [J]. Journal of Computer Applications, 2022, 42(5): 1383-1390. |
[14] | Hexuan HU, Huachao SUI, Qiang HU, Ye ZHANG, Zhenyun HU, Nengwu MA. Runoff forecast model based on graph attention network and dual-stage attention mechanism [J]. Journal of Computer Applications, 2022, 42(5): 1607-1615. |
[15] | Daili CHEN, Guoliang XU. Cross-domain person re-identification method based on attention mechanism with learning intra-domain variance [J]. Journal of Computer Applications, 2022, 42(5): 1391-1397. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||