• Journal of Information Processing Systems
• /
• v.15 no.5
• /
• pp.1218-1230
• /
• 2019

This study proposes a novel video traffic flow detection method based on machine vision technology. The three-frame difference method, which is one kind of a motion evaluation method, is used to establish initial background image, and then a statistical scoring strategy is chosen to update background image in real time. Finally, the background difference method is used for detecting the moving objects. Meanwhile, a simple but effective shadow elimination method is introduced to improve the accuracy of the detection for moving objects. Furthermore, the study also proposes a vehicle matching and tracking strategy by combining characteristics, such as vehicle's location information, color information and fractal dimension information. Experimental results show that this detection method could quickly and effectively detect various traffic flow parameters, laying a solid foundation for enhancing the degree of automation for traffic management.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.42 no.2 s.302
• /
• pp.11-20
• /
• 2005

In previous video conference system, when the number of participants in video conference increases by n, the bandwidth and memory of n2 is required. And also, it brings about increase in traffic and problem of a say during a conference in aspect of transmission of voice data. In this paper, we propose an algorithm of remote video conference using silence detection algerian to resolve the questions such as buffering method of video data in server and heavy traffic detection algorithm to the increase in participants. Video data buffering algorithm is not a method of broadcasting to other client in the server, but this algorithm uses two other methods; the buffering method of receiving compressed video data from clients and the indexing method for acquiring the video data of other participants in clients according to clients' bandwidth and network transmission speed. We apply a voice transmission algerian and a channel management algorithm to the remote video conference system. The method used in the voice transmission algorithm is a silence detection algorithm which does not send silent participants' voice data to the server. The channel management algorithm is a method allocating a say to the participants who have priority. In consideration of average 20 frames and 30ms regardless of a number of participants, we can safely conclude that the transmission of video and voice data is stable.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.12 no.2
• /
• pp.401-406
• /
• 2008

Fall event detection is one of the most common problems for elderly people, especially those living alone because falls result in serious injuries such as joint dislocations, fractures, severe head injuries or even death. In order to prevent falls or fall-related injuries, several previous methods based on video sensor showed low fall detection rates in recent years. To improve this problem and outperform the system performance, this paper presented a novel approach for fall event detection in the elderly using a subtraction between successive difference images and temporal templates in real time environment. The proposed algorithm obtained the successful detection rate of 96.43% and the low false positive rate of 3.125% even though the low-quality video sequences are obtained by a USB PC camera sensor. The experimental results have shown very promising performance in terms of high detection rate and low false positive rate.

• Journal of Korea Multimedia Society
• /
• v.10 no.5
• /
• pp.559-568
• /
• 2007

Anchor-person scene detection is of significance for video shot semantic parsing and indexing clues extraction in content-based news video indexing and retrieval system. This paper proposes an efficient algorithm extracting anchor ranges that exist in sports news video for unit structuring of sports news. To detect anchor person scenes, first, anchor person candidate scene is decided by DCT coefficients and motion vector information in the MPEG4 compressed video. Then, from the candidate anchor scenes, image processing method is utilized to classify the news video into anchor-person scenes and non-anchor(sports) scenes. The proposed scheme achieves a mean precision and recall of 98% in the anchor-person scenes detection experiment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.10a
• /
• pp.331-334
• /
• 2010

This paper proposes a new design of a video-base fire detection system using texture and color spatial distribution information. The video sequences used are taken in different days with different lighting conditions having different backgrounds. The time complexity of most previous vision-based fire detection techniques are very high due to lengthy programing. To overcome the problems of lengthy codes and time complexity, in this algorithm, at first we normalize the video image frames by size and color information. Then the spatial distribution of the color information is used to extract the candidate regions, later using visual texture of the fire, we detect the fire regions. The experimental results show an real-time fire detection over thousands of image frames, and have higher detection rate when compared to the conventional fire detection techniques.

• Journal of KIISE:Software and Applications
• /
• v.32 no.12
• /
• pp.1247-1259
• /
• 2005

This paper proposes a real-time face recognition system which detects multiple faces from low resolution video such as web-camera video. Face recognition system consists of the face detection step and the face classification step. At First, it finds face region candidates by using AdaBoost based object detection method which have fast speed and robust performance. It generates reduced feature vector for each face region candidate by using principle component analysis. At Second, Face classification used Principle Component Analysis and multi-SVM. Experimental result shows that the proposed method achieves real-time face detection and face recognition from low resolution video. Additionally, We implement the auto-tracking face recognition system using the Pan-Tilt Web-camera and radio On/Off digital door-lock system with face recognition system.

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

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.6
• /
• pp.1263-1268
• /
• 2018

In this paper, a video analysis is proposed to implement video surveillance system with deep learning object detection and probabilistic data association filter for tracking multiple objects, and suggests its implementation using GPU. The proposed video analysis technique involves object detection and object tracking sequentially. The deep learning network architecture uses ResNet for object detection and applies probabilistic data association filter for multiple objects tracking. The proposed video analysis technique can be used to detect intruders illegally trespassing any restricted area or to count the number of people entering a specified area. As a results of simulations and experiments, 48 channels of videos can be analyzed at a speed of about 27 fps and real-time video analysis is possible through RTSP protocol.

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

• Journal of Broadcast Engineering
• /
• v.9 no.2
• /
• pp.151-163
• /
• 2004

In this paper, we propose a content-based video indexing and retrieval system using MPEG-7 standard to retrieve and manage videos efficiently. The proposed system consists of video indexing module for a video DB and video retrieval module to allow various query methods on a web environment. Video indexing module stores metadata such as manually typed in keywords, automatically recognized character names, and MPEG-7 visual descriptors extracted by indexing module into a DB in a sever side. A user can access to retrieval module by a web and retrieve desired videos through various query methods like keywords, faces, example and sketch. For this retrieval system, we propose ATC(Adaptive Twin Comparison) as a cut detection method for efficient video indexing and QBME(Query By Modified Example) as an improved content-based query method for the convenience of users. Experimental results show that the proposed ATC method detects cuts well and the proposed QBME method provides the conveniences better than existing query methods such as QBE(Query By Example) and QBS(Query By Sketch).