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

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.841-846
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• 2010

This paper presents design and implementation of an intelligent video surveillance system. The proposed system has advantages of management efficiency and operation robustness unrelated to working condition compared to conventional CCTV based system. The system hardware is designed and implemented by using commercial chips such as digital media processor and video encoder, video decoder and the functions of software are to analyze temperature distribution of a infrared image and to detect disaster situation such as fire. The required functions are confirmed by testing of the prototype and we verified practicality of the system.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.161-169
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• 2011

Recent intelligent video surveillance system asks for development of more advanced technology for analysis and recognition of video data. Especially, machine learning algorithm such as Support Vector Machine (SVM) is used in order to recognize objects in video. Because SVM training demands massive amount of computation, parallel processing technique is necessary to reduce the execution time effectively. In this paper, we propose a parallel processing method of SVM training with a multi-core processor. The results of parallel SVM on a 4-core processor show that our proposed method can reduce the execution time of the sequential training by a factor of 2.5.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.165.1-170
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• 1999

Object segmentation fro natural video scenes has recently become one of very active research to pics due to the object-based video coding standard MPEG-4. Object detection and isolation is also useful for object-based indexing and search of video content, which is a goal of the emerging new standard, MPEG-7. In this paper, an automatic segmentation method of moving objects in image sequence is presented which is applicable to multimedia content authoring for MPEG-4, and two different segmentation approaches suitable for surveillance applications are addressed in raw data domain and compressed bitstream domains. We also propose an object-based video description scheme based on object segmentation for video indexing purposes.

• The KIPS Transactions:PartB
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• v.10B no.1
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• pp.95-102
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• 2003

In surveillance video system, there are many classes of images and some spatial regions are more important than other regions. The conventional compression method in this system have been compressed there full frames without classfying them depend on their important parts. To improve the accuracy of the image coding and deliver effective compression for the surveillance video system, it was necessary to separate the regions according to their importance. In this paper, we propose a new effective surveillance video image compression method. The proposed scheme defines importance based three-level region of interest block in a frame, such as background, motion object block, and the feature object block. A captured video image frame can be separated to these three different levels of block regions. And depends on the priority, each block can be modified and compressed in different resolution, compression ratio and qualify factor. Therefore, in surveillance video system, this algorithm not only reduces the image processing time and space, but also guarantees the Important image data in high quality to acquire the system's goal.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.330-333
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• 2009

This paper proposes a method to estimate the flow speed of pedestrians in surveillance videos. In the proposed method, the average moving speed of pedestrians is measured by estimating the size of real-world motion from the observed motion vectors. For this purpose, pixel-to-meter conversion factors are calculated from camera geometry. Also, the height information, which is missing because of camera projection, is predicted statistically from simulation experiments. Compared to the previous works for flow speed estimation, our method can be applied to various camera views because it separates scene parameters explicitly. Experiments are performed on both simulation image sequences and real video. In the experiments on simulation videos, the proposed method estimated the flow speed with average error of about 0.1m/s. The proposed method also showed a promising result for the real video.

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

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.2
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• pp.304-309
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• 2019

This paper presents a CMOS RF-to-DC converter for video surveillance disposable IoT applications. It widely harvests RF energy of 3G/4G cellular low-band frequency range by employing a tunable impedance matching network. The proposed converter consists of the differential-drive cross-coupled rectifier and the matching network with a 4-bit capacitor array. The proposed converter is designed using 130-nm standard CMOS process. The designed energy harvester can rectify the RF signals from 700 MHz to 900 MHz. It has a peak RF-to-DC conversion efficiency of 72.25%, 64.97%, and 66.28% at 700 MHz, 800 MHz, and 900 MHz with a load resistance of 10kΩ, respectively.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.226-233
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• 2022

Video surveillance is widely used in security surveillance, military navigation, intelligent transportation, etc. Its main research fields are pattern recognition, computer vision and artificial intelligence. This article uses OpenCV to detect and track vehicles, and monitors by establishing an adaptive model on a stationary background. Compared with traditional vehicle detection, it not only has the advantages of low price, convenient installation and maintenance, and wide monitoring range, but also can be used on the road. The intelligent analysis and processing of the scene image using CAMSHIFT tracking algorithm can collect all kinds of traffic flow parameters (including the number of vehicles in a period of time) and the specific position of vehicles at the same time, so as to solve the vehicle offset. It is reliable in operation and has high practical value.

• KIISE Transactions on Computing Practices
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• v.22 no.2
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• pp.61-68
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• 2016

Surveillance cameras have been installed in many places because security and safety has become an important issue in modern society. However, watching surveillance videos and judging accidental situations is very labor-intensive and time-consuming. So now, requests for research to automatically analyze the surveillance videos is growing. In this paper, we propose a surveillance system to track multiple persons in videos and to summarize the videos based on tracking information. The proposed surveillance summarization system applies an adaptive illumination correction, subtracts the background, detects multiple persons, tracks the persons, and saves their tracking information in a database. The tracking information includes tracking one's path, their movement status, length of staying time at the location, enterance/exit times, and so on. The movement status is classified into six statuses(Enter, Stay, Slow, Normal, Fast, and Exit). This proposed summarization system provides a person's status as a graph in time and space and helps to quickly determine the status of the tracked person.