• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.2961-2967
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• 2014

Nowadays, a private protection act has come into effect which demands for the protection of personal image information obtained by the CCTV. According to this act, the object out of interest has to be mosaicked such that it can not be identified before the image is sent to the investigation office. Meanwhile, the demand for digital videos obtained by CCTV is also increasing for digital forensic. Therefore, due to the two conflicting demands, the demand for a solution which can automatically mask an object in the CCTV video is increasing and related IT industry is expected to grow. The core technology in developing a target masking solution is the object tracking technique. In this paper, we propose an object tracking technique which suits for the application of CCTV video object masking as a postprocess. The proposed method simultaneously uses the motion and the color information to produce a stable tracking result. Furthermore, the proposed method is based on the centroid shifting method, which is a fast color based tracking method, and thus the overall tracking becomes fast.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.838-841
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• 2020

본 논문은 영상 속 폭력행위를 인식하기 위해 3 차원 컨벌루션을 활용하여 원본 영상과 스켈레톤(skeleton)영상으로부터 추출한 시각 및 움직임 정보를 동시에 활용하는 2-스트림 구조의 폭력상황 인식기를 제안한다. 제안된 폭력상황 인식기에서는 수평, 수직 방향의 큰 움직임이 많이 나타나는 폭력영상의 특성을 활용하기위해 각 방향의 특성을 독립적으로 학습할 수 있는 split-FAST 3차원 컨벌루션을 활용하고, 3 차원 Attention 을 적용하여 시각 및 움직임 정보 추출 시 영상의 중요지역을 중점적으로 반영하도록 함으로써 촬영 기기의 이동 또는 여러 사람의 뒤엉킴 등으로 영상의 시점 변화나 상황 변화가 잦은 경우에도 강인한 성능을 가질 수 있도록 하였다. 또한 기존의 연구들과 달리 비제약적인 환경에서 CCTV, 모바일 카메라 등으로 촬영된 실제 영상들로 구성된 RLVS 데이터셋을 학습 데이터로 사용함으로써 실제의 폭력 행위를 잘 인식할 수 있도록 하였다. RLVS 를 이용한 평가 실험에서 제안된 폭력상황 인식기가 약 92%의 인식 정확도를 얻었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.691-694
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• 2023

본 논문은 CCTV 화면에서의 다양한 이상상황 중 교량 데이터에 특화된 자세 추정 기반 이상탐지 알고리즘을 소개한다. 교량은 크게 도로, 인도 이렇게 두 구역으로 나눠지며, 사람들의 이동방향이 한정적이라는 특징을 가지는 장소 중 하나이다. 이러한 장소적 특징을 이용하고자 사람 자세 추정을 통해 이상의 기준을 잡고 교량 데이터에 특화된 이상탐지 알고리즘을 제안한다. CCTV 영상은 이상을 정하기 어렵고 이상에 대한 레이블이 없는 데이터가 대부분이며 이상에 대한 레이블 생성시 많은 비용 발생이 필수적이다. 본 연구에서는 이러한 한계점을 극복하고자 영상 데이터를 이미지 단위가 아닌 영상 단위로 레이블이 담긴 weakly label 을 가지는 데이터를 활용한 이상탐지 모델을 이용하였다. 특히, 교량에서의 이상상황의 특징인 사람 자세 추정으로 추출한 특질을 추가하여 기존 알고리즘의 이상탐지 예측 성능을 개선하였다.

• The Journal of Bigdata
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• v.6 no.2
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• pp.125-137
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• 2021

The number of CCTV installed in Korea is over 1.3 million, increasing by more than 15% annually. However, due to the limited budget compared to the installation demand, the infrastructure is composed of 500,000 pixel low-quality CCTV, and there is a limits on identification of objects in the video. Public CCTV has high utility in various fields such as crime prevention, traffic information collection (control), facility management, and fire prevention. Especially, since installed in high height, it works as its role in solving diverse crime and is in increasing trend. However, the current public CCTV field is operated with potential problems such as inability to identify due to environmental factors such as fog, snow, and rain, and the low-quality of collected images due to the installation of low-quality CCTV. Therefore, in this study, in order to remove the typical low-quality elements of public CCTV, the method of attenuating scattered light in the image caused by dust, water droplets, fog, etc and algorithm application method which uses deep-learning algorithm to improve input video into videos over quality over 4K are suggested.

• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.1943-1950
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• 2016

Recently, gender classification has attracted a great deal of attention in the field of video surveillance system. It can be useful in many applications such as detecting crimes for women and business intelligence. In this paper, we proposed a method which can detect pedestrians from CCTV video and classify the gender of the detected objects. So far, many algorithms have been proposed to classify people according the their gender. This paper presents a gender classification using convolutional neural network. The detection phase is performed by AdaBoost algorithm based on Haar-like features and LBP features. Classifier and detector is trained with data-sets generated form CCTV images. The experimental results of the proposed method is male matching rate of 89.9% and the results shows 90.7% of female videos. As results of simulations, it is shown that the proposed gender classification is better than conventional classification algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.169-174
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• 2018

There are many CCTV cameras connected to the video surveillance and monitoring center for the safety of citizens, and it is difficult for a few monitoring agents to monitor many channels of videos. In this paper, we propose an intelligent video surveillance system utilizing a safety map to efficiently monitor many channels of CCTV camera videos. The safety map establishes the frequency of crime occurrence as a database, expresses the degree of crime risk and makes it possible for agents of the video surveillance center to pay attention when a woman enters the crime risk area. The proposed gender classification method is processed in the order of pedestrian detection, tracking and classification with deep training. The pedestrian detection and tracking uses Adaboost algorithm and probabilistic data association filter, respectively. In order to classify the gender of the pedestrian, relatively simple AlexNet is applied to determine gender. Experimental results show that the proposed gender classification method is more effective than the conventional algorithm. In addition, the results of implementation of intelligent video security system combined with safety map are introduced.

• Journal of Korea Multimedia Society
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• v.15 no.2
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• pp.258-265
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• 2012

Our country is suffering from many human victims and property damages caused to occur great and small tidal waves in southern areas every year. Even though there were progressing many researches for storm surges, it was required more researches for detection of tidal wave and prevention system of its which can be applied in practical living fields. In this paper, we propose the disaster prevention system that can approximately detect a dangerousness of coast flooding and number of overtopping per time based on images of CCTV considering actual field application. And if it is detected a hazard of flooding of coast, the proposed detection system for tidal wave based GIS is quickly informed the areas of flooding to manager. The analyzing results of CCTV image of this proposed are derived from difference images between photos of fine day and photos or videos which are taken for the typhoon which is called "DIANMU" at our laboratory.

• Journal of Korea Multimedia Society
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• v.19 no.4
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• pp.681-688
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• 2016

In the intelligent video surveillance, recognizing the moving objects is important issue. However, the conventional moving object recognition methods have some problems, that is, the influence of light, the distinguishing between similar colors, and so on. The recognition methods for the moving objects using depth information have been also studied, but these methods have limit of accuracy because the depth camera cannot measure the depth value accurately. In this paper, we propose a recognition method for the moving objects by using both the depth and the color information. The depth information is used for extracting areas of moving object and then the color information for correcting the extracted areas. Through tests with typical videos including moving objects, we confirmed that the proposed method could extract areas of moving objects more accurately than a method using only one of two information. The proposed method can be not only used in CCTV field, but also used in other fields of recognizing moving objects.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.115-119
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• 2020

Recently, CCTV installations are rapidly increasing in the public and private sectors to prevent various crimes. In accordance with the increasing number of CCTVs, video-based abnormal behavior detection in control systems is one of the key technologies for safety. This is because it is difficult for the surveillance personnel who control multiple CCTVs to manually monitor all abnormal behaviors in the video. In order to solve this problem, research to recognize abnormal behavior using deep learning is being actively conducted. In this paper, we propose a model for detecting abnormal behavior based on the deep learning model that is currently widely used. Based on the abnormal behavior video data provided by AI Hub, we performed a comparative experiment to detect anomalous behavior through violence learning and fainting in videos using 2D CNN-LSTM, 3D CNN, and I3D models. We hope that the experimental results of this abnormal behavior learning model will be helpful in developing intelligent CCTV.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.91-105
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• 2024

Car accidents occur as a result of collisions between vehicles, leading to both vehicle damage and personal and material losses. This study developed a vehicle accident detection model based on 2,550 image frames extracted from car accident videos uploaded to YouTube, captured by CCTV. To preprocess the data, bounding boxes were annotated using roboflow.com, and the dataset was augmented by flipping images at various angles. The You Only Look Once version 8 (YOLOv8) model was employed for training, achieving an average accuracy of 0.954 in accident detection. The proposed model holds practical significance by facilitating prompt alarm transmission in emergency situations. Furthermore, it contributes to the research on developing an effective and efficient mechanism for vehicle accident detection, which can be utilized on devices like smartphones. Future research aims to refine the detection capabilities by integrating additional data including sound.