• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.12 no.3
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• pp.171-190
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• 2008

With growing applications of wireless video streaming, an efficient video traffic model featuring modern high-compression techniques is more desirable than ever, because the wireless channel bandwidths are ever limited and time-varying. We propose a modeling and analysis method for video traffic by a class of stochastic processes, which we call '$GEO^Y/G/{\infty}$ input processes'. We model video traffic by $GEO^Y/G/{\infty}$ input process with gamma-distributed batch sizes Y and Weibull-like autocorrelation function. Using four real-encoded, full-length video traces including action movies, a drama, and an animation, we evaluate our modeling performance against existing model, transformed-M/G/${\infty}$ input process, which is one of most recently proposed video modeling methods in the literature. Our proposed $GEO^Y/G/{\infty}$ model is observed to consistently provide conservative performance predictions, in terms of packet loss ratio, within acceptable error at various traffic loads of interest in practical multimedia streaming systems, while the existing transformed-M/G/${\infty}$ fails. For real-time implementation of our model, we analyze G/D/1/K queueing systems with $GEO^Y/G/{\infty}$ input process to upper estimate the packet loss probabilities.

• The Journal of the Acoustical Society of Korea
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• v.18 no.1
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• pp.72-77
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• 1999

In this paper, we propose a wavelet VQ approach to modeling VBR broadcast video traffic. The proposed method decomposes video traffic into two parts via wavelet transformation, and models each part separately. The first part, which is modeled by an AR(1) process, serves to capture the long-term trend of the traffic; the second part, classified via vector quantization, addresses the short-term behavior of the traffic. Compared with other VBR video models, our model has three advantages. First, it allows the separate modeling of long- and short-term behavior of the video traffic; second, it preserves the periodic coding structure in traffic data; and third, it provides an unified approach for the frameand slice-level traffic modeling. We demonstrate the validity of our model by statistical measurements and network performance simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.9A
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• pp.2289-2304
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• 1998

For the performance analysis and traffic control of ATM networks carrying video sequences, need an appropriate video traffic model. In this paper, we propose a new traffic model for MPEG compressed videos which are widely used for any type of video applications at th emoment. The proposed modeling scheme uses scene-based traffic characteristics and considers the correlation between frames of consecutiv GOPs. Using a simple scene detection algorithm, scene changes are modeled by state transitions and the number of GOPs of a scene state is modeled by a geometric distirbution. Frames of a scene stte are modeled by mean I, P, and B frame size. For more accurate traffic modeling, quantization errors (residual bits) that the state transition model using mean values has are compensated by autoregressive processes. We show that our model very well captures the traffic chracteristics of the original videos by performance analysis in terms of autocorrelation, histogram of frame bits genrated by the model, and cell loss rate in the ATM multiplexer with limited buffers. Our model is able to perrorm translations between levels (i.e., GOP, frame, and cell levels) and to estimate very accurately the stochastic characteristics of the original videos by each level.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.11
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• pp.1069-1076
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• 2016

Recent development in visual sensor technologies has encouraged various researches on adding imaging capabilities to sensor networks. Video data are bigger than other sensor data, so it is essential to manage the amount of image data efficiently. In this paper, a new method of video traffic estimation is proposed for efficient traffic management of visual sensor networks. In the proposed method, a first order autoregressive model is used for modeling the traffic with the consideration of the characteristics of video traffics acquired from visual sensors, and a Kalman filter algorithm is used to estimate the amount of video traffics. The proposed method is computationally simple, so it is proper to be applied to sensor nodes. It is shown by experimental results that the proposed method is simple but estimate the video traffics exactly by less than 1% of the average.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2954-2972
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• 1998

It is expected that the transport of compressed video will become a significant part of total network traffic because of the widespread introduction of multimedial services such as VOD(video on demand). Accordingly, VBR(variable bit-rate) encoded video will be widely used, due to its advantages in statistical multiplexing gain and consistent vido quality. Since the transport of video traffic requires larger bandwidth than that of voice and data, the characterization of video source and traffic modeling is very important for the design of proper resource allocation scheme in ATM networks. Suitable statistical source models are also required to analyze performance metrics such as packet loss, delay and jitter. In this paper, we analyzed and described on the characterization and traffic modeling of MPEG video sources. The models are broadly classified into two categories; i.e., statistical models and deterministic models. In statistical models, the models are categorized into five groups: AR(autoregressive), Markov, composite Marko and AR, TES, and selfsimilar models. In deterministic models, the models are categorized into $({\sigma},\;{\rho}$, parameterized model, D-BIND, and Empirical Envelopes models. Each model was analyzed for its characteristics along with corresponding advantages and shortcomings, and we made comparisons on the complexity of each model.

• 한국정보통신설비학회:학술대회논문집
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• 2005.08a
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• pp.109-112
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• 2005

In this thesis, we recommend the method for VOD service traffic modeling. By the analysis of service traffic, we verify that the process of VOD service is the POISSON process. So it is important to determine the probability of the user's existence in the system. But, because it is difficult to measure, we recommend proper using of arrival rate and service rate.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.6
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• pp.36-45
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• 2009

Recently, there are interested in the automatic traffic flowing and accident detection using various low level information from video in the road. In this paper, the automatic traffic flowing and algorithm, and application of traffic accident detection using traffic management systems are studied. To achieve these purposes, the spatio-temporal relation models using topological and directional relations have been made, then a matching of the proposed models with the directional motion verbs proposed by Levin's verbs of inherently directed motion is applied. Finally, the synonym and antonym are inserted by using WordNet. For the similarity measuring between proposed modeling and trajectory of moving object in the video, the objects are extracted, and then compared with the trajectories of moving objects by the proposed modeling. Because of the different features with each proposed modeling, the rules that have been generated will be applied to the similarity measurement by TSR (Tangent Space Representation). Through this research, we can extend our results to the automatic accident detection of vehicle using CCTV.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.2
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• pp.277-282
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• 2005

The arrangement of I-picture starting times of multiplexed variable bit .ate (VBR) MPEG videos may significantly affect the cell loss ratio (CLR) characteristics of the multiplexed traffic. This paper presents an efficient I-picture arrangement method which can minimize the CLR of the multiplexed traffic when multiple VBR MPEG videos are multiplexed onto a single constant bit rate link. In the proposed method, we use the probability that the arrival rate exceeds the link capacity as the measure for the CLR of the multiplexed traffic. Simulation results show that the proposed method can find more optimal arrangement than existing methods in respect of the CLR.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.10B
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• pp.1971-1978
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• 1999

In this paper, we study the effects of long-range dependence (LRD) in VBR video traffic on queueing system. This paper consists of Part I and II. In Part I, we present a (LRD) video traffic model based on the shifting-level (SL) process. We observe that the ACF of an empirical video trace is accurately captured by the shifting-level process with compound correlation (SLCC): an exponential function in short range and a hyperbolic function in long range. We present an accurate parameter matching algorithm for video traffic. In the Part II, we offer the queueing analysis of SL/D/1/K called ‘quantization reduction method’. Comparing the queueing performances of the DAR(1) model and the SLCC with that of a real video trace, we identify the effects of SRD and LRD in VBR video traffic on queueing performance. Simulation results show that Markoivian models can estimate network performances fairly accurately under a moderate traffic load and buffer condition, whereas LRD may have a significant effect on queueing behavior under a heavy traffic load and large buffer condition.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.23-29
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• 2014

The advanced traffic management system of intelligent transport systems automates the related traffic tasks such as vehicle speed, traffic volume and traffic incidents through the improved infrastructures like high definition cameras, high-performance radar sensors. For the safety of road users, especially, the automated incident detection and secondary accident prevention system is required. Normally, CCTV based image object detection and radar based object detection is used in this system. In this paper, we proposed the algorithm for real time highway incident detection system using multi surveillance cameras to mosaic video and track accurately the moving object that taken from different angles by background modeling. We confirmed through experiments that the video detection can supplement the short-range shaded area and the long-range detection limit of radar. In addition, the video detection has better classification features in daytime detection excluding the bad weather condition.