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

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.14 no.1
• /
• pp.169-177
• /
• 2014

It is essential to support the image and video analysis technology such as motion detection since the DVR and NVR storage were adopted in the real time visual surveillance system. Especially the network camera would be popular as a video input device. The traditional CCTV that supports analog video data get be replaced by the network camera. In this paper, we present the design and implementation of video surveillance system that provides the real time motion detection by the video storage server. The mobile application also has been implemented in order to provides the retrieval functionality of image analysis results. We develop the video analysis server with open source library OpenCV and implement the daemon process for video input processing and real-time image analysis in our video surveillance system.

• ETRI Journal
• /
• v.37 no.3
• /
• pp.551-561
• /
• 2015

We propose a novel multiple-object tracking algorithm for real-time intelligent video surveillance. We adopt particle filtering as our tracking framework. Background modeling and subtraction are used to generate a region of interest. A two-step pedestrian detection is employed to reduce the computation time of the algorithm, and an iterative particle repropagation method is proposed to enhance its tracking accuracy. A matching score for greedy data association is proposed to assign the detection results of the two-step pedestrian detector to trackers. Various experimental results demonstrate that the proposed algorithm tracks multiple objects accurately and precisely in real time.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.11 no.2
• /
• pp.248-256
• /
• 2007

Recently, the network Infrastructure grows rapidly and the digital image compression technique has made remarkable progress. Therefore, the demand of the real-time image surveillance system which uses a network camera server has been increasing. Network Camera Server has emerged as an attractive alternative to the CCTV for the wireless video surveillance. In this article, the model of JPEG Streamer for collecting and delivering JPEG image is designed and realized as a key module for the wireless video surveillance system. The thread pool and shared memory have been used to improve the stability and efficiency of the JPEG Streamer. In addition, the concept of double buffering is of much benefit to improve the quality of real-time image. In this article, the wireless video surveillance system by using JPEG Streamer is suggested to send the real-time image through the wireless internet with the personal digital assistance (PDA).

• Smart Media Journal
• /
• v.7 no.2
• /
• pp.40-46
• /
• 2018

The conventional CCTV surveillance system for preventing accidents and incidents misses 95% of the data after 22 minutes where one person monitors multiple CCTV. To address this issue, researchers have studied the computer-based intelligent video surveillance system for notifying people of the abnormal situation. However, because the system is involved in the problems of power consumption and costs, the intelligent video surveillance system based on embedded modules has been studied. This paper implements the intelligent video surveillance system based on embedded modules for detecting intruders, detecting fires and detecting loitering, falling. Moreover, the algorithm and the embedded module optimization method are applied to implement real-time processing. The intelligent video surveillance system based on embedded modules is implemented in Raspberry Pi. The algorithm processing time is 0.95 seconds on Raspberry Pi before optimization, and 0.47 seconds on Raspberry Pi after optimization, reduced processing time by 50.52%. Therefore, this suggests real processing possibility of the intelligent video surveillance system based on the embedded modules is possible.

• Proceedings of the KIEE Conference
• /
• 2005.10b
• /
• pp.337-339
• /
• 2005

The demand for a real-time image surveillance system using network camera server is increasing as the network infra has been grown and digital video compression techniques have been developed. The image surveillance system using network camera server has several merits compared to existing real-time image surveillance system using CCTV. It would be more convenient if wireless realtime image transmission were possible. In this paper, a bluetooth module is designed and implemented for a real-time image surveillance system to send and receive informations wirelessly. It may simplify the system development procedures and increase the productivity by low power consumption, low cost, and simple wireless installation. A scatter-net formation is proposed using dynamic and distributed algorithm so that the network connection is reliable.

• Journal of Ubiquitous Convergence Technology
• /
• v.1 no.1
• /
• pp.3-8
• /
• 2007

This paper presents a set of techniques used in a real-time visual surveillance system. The system is implemented on a low-cost embedded DSP platform that is designed to work with stationary video sources. It consists of detection, a tracking and a classification module. The detector uses a statistical method to establish the background model and extract the foreground pixels. These pixels are grouped into blobs which are classified into single person, people in a group and other objects by the dynamic periodicity analysis. The tracking module uses mean shift algorithm to locate the target position. The system aims to control the human density in the surveilled scene and detect what happens abnormally. The major advantage of this system is the real-time capability and it only requires a video stream without other additional sensors. We evaluate the system in the real application, for example monitoring the subway entrance and the building hall, and the results prove the system's superior performance.

• International Journal of Internet, Broadcasting and Communication
• /
• v.11 no.4
• /
• pp.76-85
• /
• 2019

We introduce a vision-based object detection method for real-time video surveillance system in low-end edge computing environments. Recently, the accuracy of object detection has been improved due to the performance of approaches based on deep learning algorithm such as Region Convolutional Neural Network(R-CNN) which has two stage for inferencing. On the other hand, one stage detection algorithms such as single-shot detection (SSD) and you only look once (YOLO) have been developed at the expense of some accuracy and can be used for real-time systems. However, high-performance hardware such as General-Purpose computing on Graphics Processing Unit(GPGPU) is required to still achieve excellent object detection performance and speed. To address hardware requirement that is burdensome to low-end edge computing environments, We propose sub-frame analysis method for the object detection. In specific, We divide a whole image frame into smaller ones then inference them on Convolutional Neural Network (CNN) based image detection network, which is much faster than conventional network designed forfull frame image. We reduced its computationalrequirementsignificantly without losing throughput and object detection accuracy with the proposed method.

• Journal of IKEEE
• /
• v.18 no.4
• /
• pp.612-619
• /
• 2014

This paper proposes a real-time multi-camera video surveillance system with automatic resolution control using motion detection. In ordinary times, it acquires 4 channels of QVGA images, and it merges them into single VGA image and transmit it. When motion is detected, it automatically increases the resolution of motion-occurring channel to VGA and decreases those of 3 other channels to QQVGA, and then these images are overlaid and transmitted. Thus, it can magnifies and watches the motion-occurring channel while maintaining transmission bandwidth and monitoring all other channels. When it is synthesized with 0.18 um technology, the maximum operating frequency is 110 MHz, which can theoretically support 4 HD cameras.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.33 no.12C
• /
• pp.1055-1061
• /
• 2008

H.264 is the prominent video coding standard in various applications such as real-time streaming and digital multimedia broadcasting, since it provides enhanced compression performance, error resilience tools, and network adaptation. Compression efficiency of H.264 has been improved, however, it requires more computing and memory access than traditional methods. In this paper we proposed adaptive intra fast algorithm for real-time video surveillance system reducing the encoding complexity of H264/A VC. For this aim, temporal interrelationship between macroblock in the previous and the current frame is used to decide the encoding mode of macroblock fast. As a result, though video quality was deteriorated a little, less than 0.04dB, and bit rate was somewhat increased in suggested method, however, proposed method improved encoding time significantly and, in particular, encoding time of an image with little changes of neighboring background such as surveillance video was more shortened than traditional methods.