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Tun, Khin Cho
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Engineering

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Localization of car license plate using adaptive Euler-template matching method Aung, Nay Zar; Peng, Jinghui; Tun, Khin Cho; Li, Songjing
Innovation in Engineering Vol. 2 No. 2 (2025): Regular Issue
Publisher : Researcher and Lecturer Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58712/ie.v2i1.30

Abstract

License plate (LP) detection plays an important role in intelligent transportation systems smart traffic control systems of today. Although it is simple and easy to implement for LP detection, traditional template matching method is less favorable compared to state-of-the-art methods due to its processing cost. Thus, this study proposes an innovative template matching method called “adaptive Euler-template matching method” for detection of LP. Two different models of Euler-template and a new matching concept are proposed. The proposed method is evaluated by detecting LP in a total of 150 test images. Then, the performance of proposed method is compared with the performances of some exiting methods. The proposed method gives accuracies of 96% using Euler-template(model-A) and 96.7% using Euler-template(model-B). The average processing time of proposed method is 0.303 s. The results show that Euler-template(model-B) is more effective for LP detection. More distinct observations are presented and finally recommendations for further works are given in this study.
Recognition human walking and running actions using temporal foot-lift features Tun, Khin Cho; Tun, Hla Myo; Win, Lei Lei Yin; Win, Khin Kyu Kyu
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.
Co-Authors Li, Songjing Nay Zar Aung Peng, Jinghui Tun, Hla Myo Win, Khin Kyu Kyu Win, Lei Lei Yin