• Proceedings of the Korean Information Science Society Conference
• /
• 2012.06a
• /
• pp.331-333
• /
• 2012

동영상 데이터에는 요약을 통하여 한눈에 알 수 있도록 하는 기술을 필요로 한다. 그 중 방송용 스트림(stream) 서비스의 경우 그 활용을 더욱 필요로 하고 있다. 여러 채널을 공유하는 TV에서는 각 채널이 무슨 방송을 하고 있는지 중요한 이슈가 된다. 이런 동영상 요약에서 키 프레임(key frame)을 찾는 기술이나 키 프레임과 거리가 먼 프레임을 찾아내는 기술이 필요하다. 이 논문에서는 키 프레임과 비 중요 프레임을 정의하고 그 프레임들을 검출하는 연구에 대하여 소개한다. 비 중요 프레임의 경우 칼라 히스토그램(color histogram)을 분석하여 실제 테스트 이미지들과의 차이점을 분석한다. 키 프레임의 경우 얼굴 검출(face detection)과 샷 변경 검출(shot change detection)의 기술을 이용하여 자동으로 추출하도록 하고 그 성능을 측정하도록 한다.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.100.3-100
• /
• 2002

This paper presents the method for detection and tracking of multiple humans robustly in mobile platform. The perception of human is performed in real time through the processing of images acquired from a moving stereo vision system. We performed multi-cue integration such as human shape, skin color and depth information to detect and track each human in moving background scene. Human shape is measured by edge-based template matching on distance transformed image. Improving robustness for human detection, we apply the human face skin color in HSV color space. And we could increase the accuracy and the robustness in both detection and tracking by applying random sampling stochastic estimati...

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2012.07a
• /
• pp.277-278
• /
• 2012

본 논문에서는 색상 군집화를 이용한 입술탐지 알고리즘을 제안한다. 이를 위해 이미 많이 알려져 있는 AdaBoost를 이용한 얼굴탐지를 수행한다. 탐지된 얼굴영역에 Lab 컬러시스템을 적용 시킨 후 입술픽셀의 특징에 따른 색상 마커를 사용하여 피부영역을 추출한다. 추출된 피부영역에 대하여 K-means 색상 군집화를 통해 입술영역을 추출한다. 그리고 실험을 통해 입술탐지 결과를 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.11a
• /
• pp.814-817
• /
• 2020

The number of single-person households is increasing every year, and there are also high concerns about the crime and safety of single-person households. In particular, crimes targeting women are increasing. Although home surveillance camera applications, which are mostly used by single-person households, only provide intrusion detection functions, this service utilizes AI image recognition technologies such as face recognition and object detection to provide theft, violence, stranger and intrusion detection. Users can receive security-related notifications, relieve their anxiety, and prevent crimes through this service.

• Smart Media Journal
• /
• v.13 no.2
• /
• pp.85-94
• /
• 2024

Artificial Intelligence of Things (AIoT), which combines AI and the Internet of Things (IoT), has recently gained popularity. Deep neural networks (DNNs) have achieved great success in many applications. Deploying complex AI models on embedded boards, nevertheless, may be challenging due to computational limitations or intelligent model complexity. This paper focuses on an AIoT-based system for smart sewing automation using edge devices. Our technique included developing a detection model and a decision tree for a sufficient testing scenario. YOLOv5 set the stage for our defective sewing stitches detection model, to detect anomalies and classify the sewing patterns. According to the experimental testing, the proposed approach achieved a perfect score with accuracy and F1score of 1.0, False Positive Rate (FPR), False Negative Rate (FNR) of 0, and a speed of 0.07 seconds with file size 2.43MB.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.1
• /
• pp.75-83
• /
• 2008

We propose a welfare interface using multiple fecial features tracking, which can efficiently implement various mouse operations. The proposed system consist of five modules: face detection, eye detection, mouth detection, facial feature tracking, and mouse control. The facial region is first obtained using skin-color model and connected-component analysis(CCs). Thereafter the eye regions are localized using neutral network(NN)-based texture classifier that discriminates the facial region into eye class and non-eye class, and then mouth region is localized using edge detector. Once eye and mouth regions are localized they are continuously and correctly tracking by mean-shift algorithm and template matching, respectively. Based on the tracking results, mouse operations such as movement or click are implemented. To assess the validity of the proposed system, it was applied to the interface system for web browser and was tested on a group of 25 users. The results show that our system have the accuracy of 99% and process more than 21 frame/sec on PC for the $320{\times}240$ size input image, as such it can supply a user-friendly and convenient access to a computer in real-time operation.

• Journal of Information Processing Systems
• /
• v.13 no.2
• /
• pp.385-399
• /
• 2017

Nowadays, with the development of signal processing technique, the protection to the integrity and authenticity of images has become a topic of great concern. A blind image authentication technology with high tamper detection accuracy for different common attacks is urgently needed. In this paper, an improved fragile watermarking method based on local binary pattern (LBP) is presented for blind tamper location in images. In this method, a binary watermark is generated by LBP operator which is often utilized in face identification and texture analysis. In order to guarantee the safety of the proposed algorithm, Arnold transform and logistic map are used to scramble the authentication watermark. Then, the least significant bits (LSBs) of original pixels are substituted by the encrypted watermark. Since the authentication data is constructed from the image itself, no original image is needed in tamper detection. The LBP map of watermarked image is compared to the extracted authentication data to determine whether it is tampered or not. In comparison with other state-of-the-art schemes, various experiments prove that the proposed algorithm achieves better performance in forgery detection and location for baleful attacks.

• Journal of Korea Society of Industrial Information Systems
• /
• v.17 no.7
• /
• pp.89-94
• /
• 2012

In fact, smoking is prohibited in elevators. It is morally wrong to smoke in elevators. In addition, smoking can be very fatal for our children and for women. In this paper, forensic evidence is submitted to court by people who smoke in elevators. Shots around the face of the person in the elevator extracted partially by scene change detection. Smokers is extracted that the white bar is at the mouth biter. People spouting smoke extraction will proceed in the future. It is extracted by using technology of color histogram, one of the scene change detection method. The extract is a much more accurate extraction ratio than the methods that do not use scene change detection.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.9C
• /
• pp.756-760
• /
• 2010

This paper is proposed to an novel facial behavior recognition system for driver's fatigue detection. Facial behavior is shown in various facial feature such as head expression, head pose, gaze, wrinkles. But it is very difficult to clearly discriminate a certain behavior by the obtained facial feature. Because, the behavior of a person is complicated and the face representing behavior is vague in providing enough information. The proposed system for facial behavior recognition first performs detection facial feature such as eye tracking, facial feature tracking, furrow detection, head orientation estimation, head motion detection and indicates the obtained feature by AU of FACS. On the basis of the obtained AU, it infers probability each state occur through Bayesian network.

• The Journal of Korea Robotics Society
• /
• v.13 no.1
• /
• pp.31-37
• /
• 2018

Recently, the safety in vehicle also has become a hot topic as self-driving car is developed. In passive safety systems such as airbags and seat belts, the system is being changed into an active system that actively grasps the status and behavior of the passengers including the driver to mitigate the risk. Furthermore, it is expected that it will be possible to provide customized services such as seat deformation, air conditioning operation and D.W.D (Distraction While Driving) warning suitable for the passenger by using occupant information. In this paper, we propose robust vehicle occupant detection algorithm based on RGB-Depth-Thermal camera for obtaining the passengers information. The RGB-Depth-Thermal camera sensor system was configured to be robust against various environment. Also, one of the deep learning algorithms, OpenPose, was used for occupant detection. This algorithm is advantageous not only for RGB image but also for thermal image even using existing learned model. The algorithm will be supplemented to acquire high level information such as passenger attitude detection and face recognition mentioned in the introduction and provide customized active convenience service.