• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.42-45
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• 2017

In this letter, we present an experimental comparative study of background subtraction algorithms for soccer video analysis. We investigated five different background subtraction algorithms under the same experimental setup. For the quantitative comparison, we employed the precision, recall, and F-measure. We believe that this comprehensive comparative study serves as a reference point and guide for developers and practitioners in choosing an appropriate background subtraction algorithm adopted for building intelligent soccer video analysis systems.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.11-16
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• 1997

This paper describes the grenade counting system developed for DPICM submunition analysis using the infrared video streams, and its some video stream processing technique. The video stream data processing procedure consists of four sequences; Analog infrared video stream recording, video stream capture, video stream pre-processing, and video stream analysis including the grenade counting. Some applications of this algorithms to real bursting test has shown the possibility of automation for submunition counting.

• Journal of Satellite, Information and Communications
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• v.10 no.2
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• pp.70-74
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• 2015

In this paper, satellite communication based video surveillance system that consisted of ultra-small aperture terminals with small-size smart vision sensor is proposed. The events such as forest fire, smoke, intruder movement are detected automatically in field and false alarms are minimized by using intelligent and high-reliable video analysis algorithms. The smart vision sensor is necessary to achieve high-confidence, high hardware endurance, seamless communication and easy maintenance requirements. To satisfy these requirements, real-time digital signal processor, camera module and satellite transceiver are integrated as a smart vision sensor-based ultra-small aperture terminal. Also, high-performance video analysis and image coding algorithms are embedded. The video analysis functions and performances were verified and confirmed practicality through computer simulation and vision sensor prototype test.

• ETRI Journal
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• v.33 no.6
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• pp.935-944
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• 2011

As a growing number of individuals are exposed to surveillance cameras, the need to prevent captured videos from being used inappropriately has increased. Privacy-related information can be protected through video encryption during transmission or storage, and several algorithms have been proposed for such purposes. However, the simple way of evaluating the security by counting the number of brute-force trials is not proper for measuring the security of video encryption algorithms, considering that attackers can devise specially crafted attacks for specific purposes by exploiting the characteristics of the target video codec. In this paper, we introduce a new attack for recovering contour information from encrypted H.264 video. The attack can thus be used to extract face outlines for the purpose of personal identification. We analyze the security of previous video encryption schemes against the proposed attack and show that the security of these schemes is lower than expected in terms of privacy protection. To enhance security, an advanced block shuffling method is proposed, an analysis of which shows that it is more secure than the previous method and can be an improvement against the proposed attack.

• Journal of Computing Science and Engineering
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• v.11 no.1
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• pp.24-31
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• 2017

Face-based video retrieval has become an active and important branch of intelligent video analysis. Face profiling and matching is a fundamental step and is crucial to the effectiveness of video retrieval. Although many algorithms have been developed for processing static face images, their effectiveness in face-based video retrieval is still unknown, simply because videos have different resolutions, faces vary in scale, and different lighting conditions and angles are used. In this paper, we combined content-based and semantic-based image analysis techniques, and systematically evaluated four mainstream local features to represent face images in the video retrieval task: Harris operators, SIFT and SURF descriptors, and eigenfaces. Results of ten independent runs of 10-fold cross-validation on datasets consisting of TED (Technology Entertainment Design) talk videos showed the effectiveness of our approach, where the SIFT descriptors achieved an average F-score of 0.725 in video retrieval and thus were the most effective, while the SURF descriptors were computed in 0.3 seconds per image on average and were the most efficient in most cases.

• Journal of Electrical Engineering and Technology
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• v.7 no.1
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• pp.115-123
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• 2012

A video analysis system used to detect events in video streams generally has several processes, including object detection, object trajectories analysis, and recognition of the trajectories by comparison with an a priori trained model. However, these processes do not work well in a complex environment that has many occlusions, mirror effects, and/or shadow effects. We propose a new approach to a context-aware video surveillance system to detect predefined contexts in video streams. The proposed system consists of two modules: a feature extractor and a context recognizer. The feature extractor calculates the moving energy that represents the amount of moving objects in a video stream and the stationary energy that represents the amount of still objects in a video stream. We represent situations and events as motion changes and stationary energy in video streams. The context recognizer determines whether predefined contexts are included in video streams using the extracted moving and stationary energies from a feature extractor. To train each context model and recognize predefined contexts in video streams, we propose and use a new ensemble classifier based on the AdaBoost algorithm, DAdaBoost, which is one of the most famous ensemble classifier algorithms. Our proposed approach is expected to be a robust method in more complex environments that have a mirror effect and/or a shadow effect.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3856-3872
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• 2018

HTTP-based adaptive streaming (HAS) has recently been widely deployed on the Internet. In the HAS system, a video content is encoded at multiple bitrates and the encoded video content is segmented into small parts of fixed durations. The HAS client requests a video segment and stores it in the playout buffer. The rate adaptation algorithm employed in HAS clients dynamically determines the video bitrate depending on the time-varying bandwidth. Many studies have shown that an efficient rate adaptation algorithm is critical to ensuring quality-of-experience in HAS systems. However, existing algorithms have problems estimating the network bandwidth because bandwidth estimation is performed on the client-side application stack. Without the help of transport layer protocols, it is difficult to achieve accurate bandwidth estimation due to the inherent segment-based transmission of the HAS. In this paper, we propose an alternative approach that utilizes the playout buffer occupancy rather than using bandwidth estimates obtained from the application layer. We start with a queueing analysis of the playout buffer. Then, we present a buffer-aware rate adaptation algorithm that is solely based on the mean buffer occupancy. Our simulation results show that compared to conventional algorithms, the proposed algorithm achieves very smooth video quality while delivering a similar average video bitrate.

• ETRI Journal
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• v.29 no.5
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• pp.569-595
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• 2007

Particularly in wireless communications, link errors severely affect the quality of the services due to the high error probability and the specific error characteristics (burst errors) in the radio access part of the network. In this work, we show that thorough analysis and appropriate modeling of radio-link error behavior are essential to evaluate and optimize higher layer protocols and services. They are also the basis for finding network-aware cross-layer processing algorithms which are capable of exploiting the specific properties of the link error statistics, such as predictability. This document presents the analysis of the radio link errors based on measurements in live Universal Mobile Telecommunication System (UMTS) radio access networks as well as new link error models originating from that analysis. It is shown that the knowledge of the specific link error characteristics leads to significant improvements in the quality of streamed video by applying the proposed novel network- and content-aware cross-layer scheduling algorithms. Although based on live UMTS network experience, many of the conclusions in this work are of general validity and are not limited to UMTS only.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.3025-3047
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• 2021

This paper presents the latest Ethernet standardization of in-vehicle network and the future trends of automotive Ethernet technology. The proposed system provides design and optimization algorithms for automotive networking technology related to AVB (Audio Video Bridge) technology. We present a design of in-vehicle network system as well as the optimization of AVB for automotive. A proposal of Reduced Latency of Machine to Machine (RLMM) plays an outstanding role in reducing the latency among devices. RLMM's approach to real-world experimental cases indicates a reduction in latency of around 41.2%. The setup optimized for the automotive network environment is expected to significantly reduce the time in the development and design process. The results obtained in the study of image transmission latency are trustworthy because average values were collected over a long period of time. It is necessary to analyze a latency between multimedia devices within limited time which will be of considerable benefit to the industry. Furthermore, the proposed reliable camera and video streaming through optimized AVB device settings would provide a high level of support in the real-time comprehension and analysis of images with AI (Artificial Intelligence) algorithms in autonomous driving.

• Journal of Multimedia Information System
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• v.8 no.1
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• pp.1-10
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• 2021

Heart Rate is a crucial physiological parameter that provides basic information about the state of the human body in the cardiovascular system, as well as in medical diagnostics and fitness assessments. At present day, it has been demonstrated that facial video-based photoplethysmographic signal captured using a low-cost RGB camera is possible to retrieve remote heart rate. Traditional heart rate measurement is mostly obtained by direct contact with the human body, therefore, it can result inconvenient for long-term measurement due to the discomfort that it causes to the subject. In this paper, we propose a non-contact-based remote heart rate measuring approach of the subject which depends on the color intensity variation of the subject's facial skin. The proposed method is applied in two regions of the subject's face, forehead and cheeks. For this, three different algorithms are used to measure the heart rate. i.e., Fast Fourier Transform (FFT), Independent Component Analysis (ICA) and Principal Component Analysis (PCA). The average accuracy for the three algorithms utilizing the proposed method was 89.25% in both regions. It is also noteworthy that the FastICA algorithm showed a higher average accuracy of more than 92% in both regions. The proposed method obtained 1.94% higher average accuracy than the traditional method based on average color value.