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All Journal Innovation in Engineering Research on Instrumentation
Lei Lei Yin Win
Department of Electronic Engineering, Yangon Technological University, Yangon, MYANMAR
Engineering

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Recognition human walking and running actions using temporal foot-lift features Khin Cho Tun; Hla Myo Tun; Lei Lei Yin Win; Khin Kyu Kyu Win
Innovation in Engineering Vol. 1 No. 1 (2024): Regular Issue
Publisher : Researcher and Lecturer Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58712/ie.v1i1.1

Abstract

The recognition of human walking and running actions becomes essential part of many different practical applications such as smart video-surveillance, patient and elderly people monitoring, health care as well as human-robot interaction. However, the requirements of a large spatial information and a large number of frames for each recognition phase are still open challenges. Aiming at reducing the number frames and joint information required, temporal foot-lift features were introduced in this study. The temporal foot-lift features and weighted KNN classifier were used to recognize “Walkin and“Running”actions from four different human action datasets. Half of the datasets were trained and the other half of datasets were experimentally tested for performance evaluation. The experimental results were presented and explained with justifications. An overall recognition accuracy of 88.6% was achieved using 5 frames and it was 90.7% when using 7 frames. The performance of proposed method was compared with the performances of existing methods. Skeleton joint information and temporal foot-lift features are promising features for real-time human moving action recognition.
Comparative Analysis of Siren Classification Technique for Emergency Vehicles Ei Paing Phyo; Hla Myo Tun; Thanda Win; Lei Lei Yin Win
Research on Instrumentation Vol. 1 No. 1 (2024): Research on Instrumentation
Publisher : RESSTECH

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66926/rins.2024.4

Abstract

Emergency vehicle sirens greatly aid traffic control and public safety awareness. Improving emergency response systems requires accurate siren classification. This study aims to categorize emergency vehicles, particularly fire trucks, police cars, and ambulances, based on the features of their sirens. It thoroughly analyses various schemes for categorizing emergency vehicle sirens. Mel-Frequency Cepstral Coefficients (MFCC), Zero-Crossing Rate (ZCR), Spectral Centroid, and hybrid methods that combine MFCC with ZCR and Spectral Centroid were observed for comparison. The data set is sourced from the Google Audio Set Ontology, ensuring robust training and evaluation of the models. This methodology involves preprocessing audio data, extracting relevant features, and training classifiers. The proposed hybrid method combines MFCC with Spectral Centroid to leverage their complementary strengths. Through rigorous experimentation, this system evaluates the performance of different classifiers, aiming to provide insights for optimal siren classification. The findings contribute to advancing audio classification methodologies and have implications for developing more robust emergency response and traffic management systems.
Co-Authors Ei Paing Phyo Hla Myo Tun Khin Cho Tun Khin Kyu Kyu Win Thanda Win