Human Activity Recognition Based on Transfer Learning with Spatio-Temporal Representations

Author:

Zebhi Saeedeh,Almodarresi SMT,Abootalebi Vahid

Abstract

A Gait History Image (GHI) is a spatial template that accumulates regions of motion into a single image in which moving pixels are brighter than others. A new descriptor named Time-Sliced Averaged Gradient Boundary Magnitude (TAGBM) is also designed to show the time variations of motion. The spatial and temporal information of each video can be condensed using these templates. Based on this opinion, a new method is proposed in this paper. Each video is split into N and M groups of consecutive frames, and the GHI and TAGBM are computed for each group, resulting spatial and temporal templates. Transfer learning with the fine-tuning technique has been used for classifying these templates. This proposed method achieves the recognition accuracies of 96.50%, 92.30% and 97.12% for KTH, UCF Sport and UCF-11 action datasets, respectively. Also it is compared with state-of-the-art approaches and the results show that the proposed method has the best performance.

Publisher

Zarqa University

Subject

General Computer Science

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Transfer Learning Models for CNN Fusion With Fisher Vector for Codebook Optimization of Foreground Features;IEEE Access;2024

2. A Domain Adaptive Feature Relevance Analysis for Human Activity Recognition using Transfer Learning;2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE);2023-11-01

3. Modified Cuckoo Search Algorithm for Motion Vector Estimation;The International Arab Journal of Information Technology;2023

4. A Comparative Study on Deep Learning and Machine Learning Models for Human Action Recognition in Aerial Videos;The International Arab Journal of Information Technology;2023

5. Activity Recognition from Videos Using Semantic Motion Patterns;Lecture Notes in Electrical Engineering;2023

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