• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.6-15
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• 2022

In this study, a traffic light recognition algorithm was implemented and validated for low-speed special purpose vehicles in an urban environment. Real-time image data using a camera and YOLO algorithm were applied. Two methods were presented to increase the accuracy of the traffic light recognition algorithm, and it was confirmed that the second method had the higher accuracy according to the traffic light type. In addition, it was confirmed that the optimal YOLO algorithm was YOLO v5m, which has over 98% mAP values and higher efficiency. In the future, it is thought that the traffic light recognition algorithm can be used as a dual system to secure the platform safety in the traffic information error of C-ITS.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.821-827
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• 2009

Human emotion can be reflected by their facial expressions. So, it is one of good ways to understand people's emotions by recognizing their facial expressions. General recognition system of facial expressions had selected interesting points, and then only extracted features without analyzing physical meanings. They takes a long time to find interesting points, and it is hard to estimate accurate positions of these feature points. And in order to implement a recognition system of facial expressions on real-time embedded system, it is needed to simplify the algorithm and reduce the using resources. In this paper, we propose a real-time recognition algorithm of facial expressions that project the grid points on an expression space based on Gabor wavelet feature. Facial expression is simply described by feature vectors on the expression space, and is classified by an neural network with its resources dramatically reduced. The proposed system deals 5 expressions: anger, happiness, neutral, sadness, and surprise. In experiment, average execution time is 10.251 ms and recognition rate is measured as 87~93%.

• Journal of International Society for Simulation Surgery
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• v.2 no.2
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• pp.64-66
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• 2015

Purpose : In this paper we proposed cascade feature filter and projection method for rapid human face recognition for the large-scale high-dimensional face database. Materials and Methods : The relevant features are selected from the large feature set using Fast Correlation-Based Filter method. After feature selection, project them into discriminant using Principal Component Analysis or Linear Discriminant Analysis. Their cascade method reduces the time-complexity without significant degradation of the performance. Results : In our experiments, the ORL database and the extended Yale face database b were used for evaluation. On the ORL database, the processing time was approximately 30-times faster than typical approach with recognition rate 94.22% and on the extended Yale face database b, the processing time was approximately 300-times faster than typical approach with recognition rate 98.74 %. Conclusion : The recognition rate and time-complexity of the proposed method is suitable for real-time face recognition system on the large-scale high-dimensional face database.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.124-129
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• 2013

This paper proposes a high-performance vector inner product calculation circuit for real-time object recognition based on SVM algorithm. SVM algorithm shows a higher detection rate than other object recognition algorithms. However, it requires a huge amount of computational efforts. Since vector inner product calculation is one of the major operations of SVM algorithm, it is important to implement a high-performance vector inner product calculation circuit for real-time object recognition capability. The proposed circuit adopts the pipeline architecture with six stages to increase the operating speed and makes it possible to recognize objects in real time based on SVM. The proposed circuit was described in Verilog HDL at RTL. For silicon verification, an MPW chip was fabricated using TSMC 180nm standard cell library. The operation of the implemented MPW chip was verified on the test board with test application software developed for the chip verification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.172-174
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• 2022

Logistics processing companies in Korea are mostly handling various types of products in and out. In order to process various types of products, the sorting business is performed by hand. In this paper, we propose a real-time QR code detection method using a vision sensor to achieve high efficiency with a small amount of manpower. The limiting system uses a vision sensor to process QR code recognition of logistics in real time. The proposed system can quickly identify a large number of QR codes through multiple recognition rather than QR code recognition, which is a single part of logistics. In the study, the system was actually implemented and verified, and multiple QR recognition was confirmed in the image through the vision center.

• Journal of information and communication convergence engineering
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• v.12 no.4
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• pp.263-270
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• 2014

Corner detection and feature extraction are essential aspects of computer vision problems such as object recognition and tracking. Feature detectors such as Scale Invariant Feature Transform (SIFT) yields high quality features but computationally intensive for use in real-time applications. The Features from Accelerated Segment Test (FAST) detector provides faster feature computation by extracting only corner information in recognising an object. In this paper we have analyzed the efficient object detection algorithms with respect to efficiency, quality and robustness by comparing characteristics of image detectors for corner detector and feature extractors. The simulated result shows that compared to conventional SIFT algorithm, the object recognition system based on the FAST corner detector yields increased speed and low performance degradation. The average time to find keypoints in SIFT method is about 0.116 seconds for extracting 2169 keypoints. Similarly the average time to find corner points was 0.651 seconds for detecting 1714 keypoints in FAST methods at threshold 30. Thus the FAST method detects corner points faster with better quality images for object recognition.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.39-42
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• 2023

Recently, with the explosive increase of video streaming services, real-time live broadcasting has also increased, which leads to an infringement problem for user privacy. So, to solve such problems, we proposed the real image blurring system using dlib face-recognition library. 68 face landmarks are extracted and convert into 128 vector values. After that the proposed system tries to compare this value with the image in the database, and if it is over 0.45, it is considered as different person and image blurring processing is performed. With the proposed system, it is possible to solve the problem of user privacy infringement, and also to be utilized to detect the specific person. Through experimental results, the proposed system has an accuracy of more than 90% in terms of face recognition.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.120-123
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• 2020

The surveillance system to prevent crime and accidents in advance has become a necessity, not an option in real life. Not only public institutions but also individuals are installing surveillance cameras to protect their property and privacy. However, since the installed surveillance camera cannot be monitored for 24 hours, the focus is on the technology that tracks the video after an accident occurs rather than prevention. In this paper, we propose a system model that monitors abnormal behaviors that may cause crimes through real-time video, and when a specific behavior occurs, the surveillance system automatically detects it and responds immediately through an alarm. We are a model that analyzes real-time images from surveillance cameras and uses I3D models from analysis servers to analyze abnormal behavior and deliver notifications to web servers and then to clients. If the system is implemented with the proposed model, immediate response can be expected when a crime occurs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.5
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• pp.877-889
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• 2006

This paper introduces a simple but effective method for recognizing vehicle models corresponding to each maker by information and images for moving vehicles. The proposed approach is implemented by combination of the breadth detection mechanism using the vehicle's pressure, exact height detection by a laser scanning, and license plate recognition for classifying specific vehicles. The implemented system is therefore capable of robust classification with real-time vehicle's moving images and established sensors. Simulation results using the proposed method on synthetic data as well as real world images demonstrate that proposed method can maintain an excellent recognition rate for moving vehicle models because of image acquisition by 2-D CCD and various image processing algorithms.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.155-158
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• 2000

In this paper, For operator's conveniency of the mobile robot, achieved the system which control the robot by adopting the speaker independently isolated word recognition and by implementing the real time with TMS320C31. and This paper using the Tri-ultrasonics range finder to detect obstacles and implements the mobile robot. In this paper, DSP processor (TMS320C31) is used signal processing for speech recognition in the real time and Micro processor(80C196KC) is controling the ultrasonics range finders.