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Multi-Style License Plate Recognition System using K-Nearest Neighbors
( Soungsill Park ),( Hyoseok Yoon ),( Seho Park ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.5
There are various styles of license plates for different countries and use cases that require style-specific methods. In this paper, we propose and illustrate a multi-style license plate recognition system. The proposed system performs a series of processes for license plate candidates detection, structure classification, character segmentation and character recognition, respectively. Specifically, we introduce a license plate structure classification process to identify its style that precedes character segmentation and recognition processes. We use a K-Nearest Neighbors algorithm with pre-training steps to recognize numbers and characters on multi-style license plates. To show feasibility of our multi-style license plate recognition system, we evaluate our system for multi-style license plates covering single line, double line, different backgrounds and character colors on Korean and the U.S. license plates. For the evaluation of Korean license plate recognition, we used a 50 minutes long input video that contains 138 vehicles of 6 different license plate styles, where each frame of the video is processed through a series of license plate recognition processes. From two experiments results, we show that various LP styles can be recognized under 50 ms processing time and with over 99% accuracy, and can be extended through additional learning and training steps.
Key Posture Extraction Method using a Small Number of Motion Datasets
Soungsill Park,Youngho Chai 중앙대학교 영상콘텐츠융합연구소MINT 2023 Moving Image & Technology (MINT) Vol.3 No.1
Although human movement can be inferred from various datasets, a method is required to recognize the change in movement from a small amount of new data. A deep learning-based method cannot be used to learn motion from limited data. To use existing feature extraction methods, a process is required to extract key postures from the entire dataset. The proposed method extracts key postures from an entire frame, whereby the differences in distance from the starting posture are added and displayed as a graph. The key task is to determine an inflection point in the graph, divide the data, and designate the frame corresponding to the inflection as an intermediate frame. Kinect and inertial sensor data are used to demonstrate the results of the proposed method. The proposed method can be used to determine similar motions on the basis of key postures, using existing datasets.