Fine-Tuned Temporal Dense Sampling with 1D Convolutional Neural Network for Human Action Recognition


Lim, Kian Ming and Lee, Chin Poo and Tan, Kok Seang and Alqahtani, Ali and Ali, Mohammed (2023) Fine-Tuned Temporal Dense Sampling with 1D Convolutional Neural Network for Human Action Recognition. Sensors, 23 (11). p. 5276. ISSN 1424-8220

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Human action recognition is a constantly evolving field that is driven by numerous applications. In recent years, significant progress has been made in this area due to the development of advanced representation learning techniques. Despite this progress, human action recognition still poses significant challenges, particularly due to the unpredictable variations in the visual appearance of an image sequence. To address these challenges, we propose the fine-tuned temporal dense sampling with 1D convolutional neural network (FTDS-1DConvNet). Our method involves the use of temporal segmentation and temporal dense sampling, which help to capture the most important features of a human action video. First, the human action video is partitioned into segments through temporal segmentation. Each segment is then processed through a fine-tuned Inception-ResNet-V2 model, where max pooling is performed along the temporal axis to encode the most significant features as a fixed-length representation. This representation is then fed into a 1DConvNet for further representation learning and classification. The experiments on UCF101 and HMDB51 demonstrate that the proposed FTDS-1DConvNet outperforms the state-of-the-art methods, with a classification accuracy of 88.43% on the UCF101 dataset and 56.23% on the HMDB51 dataset.

Item Type: Article
Uncontrolled Keywords: human action recognition; temporal dense sampling; 1D convolutional neural network (1D ConvNet); 1D-CNN; Inception-ResNet-V2
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75-76.95 Calculating machines
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 03 Jul 2023 03:39
Last Modified: 03 Jul 2023 03:39


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