An enhanced 3DCNN‐ConvLSTM for spatiotemporal multimedia data analysis. (16th April 2019)

• Record Type: Journal Article
• Authors: Wang, Tian; Li, Jiakun; Zhang, Mengyi; Zhu, Aichun; Snoussi, Hichem; Choi, Chang
• Other Names: Ko Hoon guestEditor.; Marreiros Goreti guestEditor.; Cong Vinh Phan guestEditor.
• Is Part Of: Concurrency and computation. Volume 33:Number 2(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-04-16
• Subjects: action recognition -- ConvLSTM -- 3DCNN; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5302
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: 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.