An enhanced 3DCNN‐ConvLSTM for spatiotemporal multimedia data analysis

Tian Wang (1) , Jiakun Li (1) , Mengyi Zhang (2) , Aichun Zhu (2) , Hichem Snoussi (3) , Chang Hyuck Choi (4)

1 BUAA - Beihang University
2 NJUST - Nanjing University of Science and Technology
3 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
4 CU - Chosun University

At present, human action recognition is a challenging and complex task in the field of computer vision. The combination of CNN and RNN is a common and effective network structure for this task. Especially, we use 3DCNN in CNN part and ConvLSTM in RNN part. We divide the video into multiple temporal segments by average and compress each segment into one feature map by pooling layer. Adding the pooling layer, dropout layer, and batch normalization layer into ConvLSTM is our groundbreaking work. We test our model on KTH, UCF‐11, and HMDB51 datasets and achieve a high accuracy of action recognition.

Domaines: Recherche opérationnelle [math.OC]

Tian Wang, Jiakun Li, Mengyi Zhang, Aichun Zhu, Hichem Snoussi, et al.. An enhanced 3DCNN‐ConvLSTM for spatiotemporal multimedia data analysis. Concurrency and Computation: Practice and Experience, 2019, pp.e5302. ⟨10.1002/cpe.5302⟩. ⟨hal-02297518⟩