电子学报 ›› 2020, Vol. 48 ›› Issue (7): 1269-1275.DOI: 10.3969/j.issn.0372-2112.2020.07.004
丰艳, 张甜甜, 王传旭
收稿日期:
2019-08-13
修回日期:
2020-04-06
出版日期:
2020-07-25
发布日期:
2020-07-25
作者简介:
丰艳 女,1977年10月出生,山东曲阜人.现为青岛科技大学副教授,硕士生导师.主要从事虚拟现实、计算机视觉方面的研究.E-mail:fywmh@163.com;张甜甜 女,1993年3月出生,山东烟台人.2017年毕业于齐鲁工业大学信息学院,取得计算机科学与技术专业学士学位,现为青岛科技大学信息学院在读硕士研究生,从事计算机视觉方面的有关研究.E-mail:zhangtt0424@163.com;王传旭 男,1968年1月出生,山东邹城人.教授、硕士生导师.1990年、2000年和2007年分别在中国石油大学(华东)、中国石油大学(北京)和中国海洋大学获应用电子技术学士、硕士和博士学位.主要从事计算机视觉方面的有关研究.E-mail:Wangchuanxu_qd@163.com
基金资助:
FENG Yan, ZHANG Tian-tian, WANG Chuan-xu
Received:
2019-08-13
Revised:
2020-04-06
Online:
2020-07-25
Published:
2020-07-25
摘要: 针对复杂场景下群组行为特征的多样性以及交互关系难以建模的问题,提出一种全新的分层网络架构.第一层网络,利用伪3D残差网络与图卷积网络相结合捕获交互关系特征;第二层网络,利用伪3D残差网络捕获群组全局场景时空特征.根据上述特征之间的互补作用对它们的群组行为决策输出,提出一种权重自适应调整决策融合算法,对上面两层网络的群组行为类别自适应计算重要性权重,实现决策融合.该方法在CAD和CAE上分别取得了91.4%和97.9%的平均识别精度.
中图分类号:
丰艳, 张甜甜, 王传旭. 基于伪3D残差网络与交互关系建模的群组行为识别方法[J]. 电子学报, 2020, 48(7): 1269-1275.
FENG Yan, ZHANG Tian-tian, WANG Chuan-xu. Group Activity Recognition Method Based on Pseudo 3D Residual Network and Interaction Modeling[J]. Acta Electronica Sinica, 2020, 48(7): 1269-1275.
[1] 韩磊,李君峰,贾云得.基于时空单词的两人交互行为识别方法[J].计算机学报,2010,33(4):776-784. HAN Lei,LI Jun-feng,JIA Yun-de.Human interaction recognition using spatio-temporal words[J].Chinese Journal of Computers,2010,33(4):776-784.(in Chinese) [2] 朱煜,赵江坤,王逸宁,郑兵兵.基于深度学习的人体行为识别算法综述[J].自动化学报,2016,42(6):848-857. ZHU Yu,ZHAO Jiang-kun,WANG Yi-ning,ZHENG Bing-bing.A review of human action recognition based on deep learning[J].Acta Automatica Sinica,2016,42(6):848-857.(in Chinese) [3] 郑兴华,孙喜庆,吕嘉欣,等.基于深度学习和智能规划的行为识别[J].电子学报,2019,47(8):1661-1668. ZHENG Xing-hua,SUN Xi-qing,LU Jia-xin,et al.Action recognition based on deep learning and artificial intelligence planning[J].Acta Electronica Sinica,2019,47(8):1661-1668.(in Chinese) [4] 王传旭,刘云,厉万庆.基于时空特征点的非监督姿态建模和行为识别的算法研究[J].电子学报,2011,39(8):1751-1756. WANG Chuan-xu,LIU Yun,LI Wan-qing.Research ofunsupervised posture modeling and action recognition based on spatial-temporal interesting points[J].Acta Electronica Sinica,2011,39(8):1751-1756.(in Chinese) [5] Deng Z,Vahdat A,Hu H,et al.Structure inference machines:Recurrent neural networks for analyzing relations in group activity recognition[A].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C].USA:IEEE,2016.4772-4781. [6] Tran D,Bourdev L,Fergus R,et al.Learning spatiotemporal features with 3d convolutional networks[A].Proceedings of the IEEE International Conference on Computer Vision[C]. USA:IEEE,2015.4489-4497. [7] Simonyan K,Zisserman A.Two-stream convolutional networks for action recognition in videos[A].Advances in Neural Information Processing Systems[C].USA:Massachusetts Institute of Technology Press,2014.568-576. [8] Ibrahim M S,Muralidharan S,Deng Z,et al.A hierarchical deep temporal model for group activity recognition[A].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C].USA:IEEE,2016.1971-1980. [9] Vahora S,Chauhan N.Deep neural network model for group activity recognition using contextual relationship[J].Engineering Science and Technology,an International Journal,2019,22(1):47-54. [10] Ramanathan V,Huang J,Abu-El-Haija S,et al.Detecting events and key actors in multi-person videos[A].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C].USA:IEEE,2016.3043-3053. [11] Li W,Chang M-C,Lyu S.Who did what at where and when:simultaneous multi-person tracking and activity recognition[J].arXiv Preprint,2018,arXiv:1807.01253. [12] Deng Z,Zhai M,Chen L,et al.Deep structured models for group activity recognition[J].arXiv Preprint,2015,arXiv:1506.04191. [13] Wu J,Wang L,Wang L,et al.Learning actor relation graphs for group activity recognition[A].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C].USA:IEEE,2019.9964-9974. [14] 罗会兰,王婵娟.行为识别中一种基于融合特征的改进VLAD编码方法[J].电子学报,2019,47(1):49-58. LUO Hui-lan,WANG Chan-juan.An improved VLAD coding method based on fusion feature in action recognition[J].Acta Electronica Sinica,2019,47(1):49-58.(in Chinese) [15] 田国会,尹建芹,闫云章,李国栋.基于混合高斯模型和主成分分析的轨迹分析行为识别方法[J].电子学报,2016,44(1):143-149. TIAN Guo-hui,YIN Jian-qin,YAN Yun-zhang,LI Guo-dong.Gaussian mixture models and principal component analysis based human trajectory behavior recognition[J].Acta Electronica Sinica,2016,44(1):143-149.(in Chinese) [16] Cao Z,Simon T,Wei S-E,et al.Realtime multi-person 2d pose estimation using part affinity fields[A].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition[C].USA:IEEE,2017.7291-7299. [17] Qiu Z,Yao T,Mei T.Learning spatio-temporal representation with pseudo-3d residual networks[A].Proceedings of the IEEE International Conference on Computer Vision[C].USA:IEEE,2017.5533-5541. [18] Choi W,Shahid K,Savarese S.What are they doing?:Collective activity classification using spatio-temporal relationship among people[A].IEEE 12th International Conference on Computer Vision(ICCV) Workshops[C].USA:IEEE,2009.1282-1289. [19] Choi W,Shahid K,Savarese S.Learning context for collective activity recognition[A].Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern RecognitionJune(CVPR'11)[C].USA:IEEE,2011.3273-3280. [20] Li X,Choo Chuah M.SBGAR:semantics based group activity recognition[A].Proceedings of the IEEE International Conference on Computer Vision[C].USA:IEEE,2017.2876-2885. [21] Lan T,Wang Y,Yang W,et al.Discriminative latent models for recognizing contextual group activities[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,34(8):1549-1562. [22] Choi W,Savarese S.Understanding collective activitiesof people from videos[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,36(6):1242-1257. [23] Amer M R,Lei P,Todorovic S.Hirf:Hierarchical random field for collective activity recognition in videos[A].European Conference on Computer Vision[C].Cham:Springer,2014.572-585. |
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