• Journal of IKEEE
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• v.26 no.1
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• pp.133-136
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• 2022

This paper is about motion gesture recognition system, and proposes the following improvement to the flaws of the current system: a motion gesture recognition system and such algorithm that uses the video image of the entire hand and reading its motion gesture to advance the accuracy of recognition. The motion gesture recognition system includes, an image capturing unit that captures and obtains the images of the area applicable for gesture reading, a motion extraction unit that extracts the motion area of the image, and a hand gesture recognition unit that read the motion gestures of the extracted area. The proposed application of the motion gesture algorithm achieves 20% improvement compared to that of the current system.

• Journal of Korea Multimedia Society
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• v.13 no.11
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• pp.1630-1642
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• 2010

Recently, there is an increasing need for developing the technique of Hand Gesture Recognition (HGR), for vision based interface. Since hand gesture is defined as consecutive change of hand posture, developing the algorithm of Hand Posture Recognition (HPR) is required. Among the factors that decrease the performance of HPR, we focus on rotation factor. To achieve rotation invariant HPR, we propose a method that uses the property of video that adjacent frames in video have high correlation, considering the environment of HGR. The proposed method introduces template update of object tracking using the above mentioned property, which is different from previous works based on still images. To compare our proposed method with previous methods such as template matching, PCA and LBP, we performed experiments with video that has hand rotation. The accuracy rate of the proposed method is 22.7%, 14.5%, 10.7% and 4.3% higher than ordinary template matching, template matching using KL-Transform, PCA and LBP, respectively.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.3
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• pp.155-160
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• 2017

Recently, Deep learning has been used successfully to solve many recognition problems. It has many advantages over existing machine learning methods that extract feature points through hand-crafting. Deep neural networks for human activity recognition split video data into frame images, and then classify activities by analysing the connectivity of frame images according to the time. But it is difficult to apply to actual problems which has small-scale activity classes. Because this situations has a problem of overfitting and insufficient training data. In this paper, we defined 5 type of small-scale human activities, and classified them. We construct video database using 700 video clips, and obtained a classifying accuracy of 74.00%.

• Journal of Ocean Engineering and Technology
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• v.35 no.1
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• pp.91-97
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• 2021

Marine accidents caused by ships have brought about economic and social losses as well as human casualties. Most of these accidents are caused by small and medium-sized ships and are due to their poor conditions and insufficient equipment compared with larger vessels. Measures are quickly needed to improve the conditions. This paper discusses a video-integrated collision prediction and fall detection system to support the safe navigation of small- and medium-sized ships. The system predicts the collision of ships and detects falls by crew members using the CCTV, displays the analyzed integrated information using automatic identification system (AIS) messages, and provides alerts for the risks identified. The design consists of an object recognition algorithm, interface module, integrated display module, collision prediction and fall detection module, and an alarm management module. For the basic research, we implemented a deep learning algorithm to recognize the ship and crew from images, and an interface module to manage messages from AIS. To verify the implemented algorithm, we conducted tests using 120 images. Object recognition performance is calculated as mAP by comparing the pre-defined object with the object recognized through the algorithms. As results, the object recognition performance of the ship and the crew were approximately 50.44 mAP and 46.76 mAP each. The interface module showed that messages from the installed AIS were accurately converted according to the international standard. Therefore, we implemented an object recognition algorithm and interface module in the designed collision prediction and fall detection system and validated their usability with testing.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.3
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• pp.99-112
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• 2015

Digital imaging technology has advanced beyond the limits of the multimedia industry IT convergence, and to develop a complex industry, particularly in the field of object recognition, face smart-phones associated with various Application technology are being actively researched. Recently, face recognition technology is evolving into an intelligent object recognition through image recognition technology, detection technology, the detection object recognition through image recognition processing techniques applied technology is applied to the IP camera through the 3D image object recognition technology Face Recognition been actively studied. In this paper, we first look at the essential human factor, technical factors and trends about the technology of the human object recognition based SPFACS(Smile Progress Facial Action Coding System)study measures the smile detection technology recognizes multi-faceted object recognition. Study Method: 1)Human cognitive skills necessary to analyze the 3D object imaging system was designed. 2)3D object recognition, face detection parameter identification and optimal measurement method using the AAM algorithm inside the proposals and 3)Face recognition objects (Face recognition Technology) to apply the result to the recognition of the person's teeth area detecting expression recognition demonstrated by the effect of extracting the feature points.

• Journal of Korea Multimedia Society
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• v.12 no.9
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• pp.1252-1261
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• 2009

One notable challenge in video searching and summarizing is extracting semantic from video contents and annotating context for video contents. Video semantic or context could be obtained by two methods to extract objects and contexts between objects from video. However, the method that use just to extracts objects do not express enough semantic for shot or scene as it does not describe relation and interaction between objects. To be more effective, after extracting some objects, context like relation and interaction between objects needs to be extracted from conversation situation. This paper is a study for how to detect speaker and how to compose context for talking to annotate conversation context. For this, based on this study, we proposed the methods that characters are recognized through face recognition technology, speaker is detected through mouth motion, conversation context is extracted using the rule that is composed of speaker existing, the number of characters and subtitles existing and, finally, scene context is changed to xml file and saved.

• Journal of Broadcast Engineering
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• v.9 no.2
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• pp.164-173
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• 2004

In this paper, we propose a system that can analyze appearance interval of casts by detecting and recognizing casts in broadcasting videos. The cast is one of the most important characteristics in broadcasting videos such as drama and sports. In this paper, we propose the ACAV(Automatic Cast-list Analysis in Videos) system that analyzes cast-list automatically in video. The ACAV system consists of FAGIS(FAce reGIStration) which registers detected faces into the face DB and FACOG(FAce reCOGnition) that analyses the cast-list in video sequence using the face DB. We evaluate performance of the ACAV system by comparing with FaceIt, one of the most well-known commercial systems for the cast-list analysis. The ACAV shows face detection and recognition rates of 84.3% and 75.7% that are about 30% and 27.5% higher than those of FaceIt, respectively. The ACAV system can be applied to mass broadcasting videos management system for broadcasters and video management system of PVR(Personal Video Recorder) and mobile phone for the public.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1375-1378
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• 1997

In this paper, we presents the algorithm which is to recognize the traffic sign on the road the traffic signal in a video image for autonomous navigation. First, the rocognition of traffic sign on the road can be detected using boundary point estimation form some scan-lines within the lane deducted. For this algorithm, index matrix method is used to detemine what sign is. Then, the traffic signal recognition is performed by usign the window minified by several scan-lines which position may be expected. For this algoritm, line profile concept is adopted.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1209-1214
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• 2016

LPR(License plate recognition) is a kind of the most popular surveillance technology based on accompanied by a video and video within the optical character recognition. LPR need a many process. One is a localization of car license plates, license plate of size, space, contrast, normalized to adjust the brightness, another is character division for recognize the character optical character recognition to win the individual characters, character recognition, the other is phrase analysis of the shape, size, position by year, the procedure for the analysis by comparing the database of license plate having a difference by region. In this paper, describing the results of performance of license plate recognition S/W, which was implemented using EmguCV, find the location, using the tesseract OCR, which are well known to an optical character recognition engine of open source, the characters of the license plate image capturing angle of the plate, image size, brightness.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.11
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• pp.93-100
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• 2005

In this paper, we implement the embedded speech recognition system to support various application services such as audio and video control using speech recognition interface on cars. The embedded speech recognition system is implemented and ported in a DSP board. Because MIC type and speech codecs affect the accuracy of speech recognition. And also, we optimize the simulation and test environment to effectively remove the real noises on a car. We applied a noise suppression and feature compensation algorithm to increase an accuracy of sppech recognition on a car. And we used a context dependent tied-mixture acoustic modeling. The performance evaluation showed high accuracy of proposed system in office environment and even real car environment.