• 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.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.101-104
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• 2001

Over the last few years, streaming audio and video content on Internet sites has increased at unprecedented rates. The predominant method of delivering video over the current Internet is video streaming such as SureStream or Intelligent Stream. Since each method provides the client with only one data stream from one server, it often suffers from poor qualify of pictures in the case of network link congestion. In this paper, we propose a novel method of delivering video stream based on wavelet to a client by utilizing multi-threaded parallel connections from the client to multiple servers and to provides a better way to address the scalability functionalities. The experimental results show that the video quality delivered by the proposed multithreaded stream could significantly be improved over the conventional single video stream methods.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1320-1331
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• 2004

A video adaptation system translates the source video stream into appropriate video stream while satisfying the network and client constraints and maximizing the video quality as much as possible. This paper proposes a semi-dynamic video adaptation scheme, in which several intermediate video streams and the information for the measuring of video quality are generated statically. The intermediate video streams are generated by reducing the resolution of the video stream by a power of two several times, and they are stored as the intermediate video streams on the video server. The statically generated information for the input video stream consists of the degrees of smoothness for each frame rate and the degree of frame definition for each pixel bit rate. It helps to dynamically generate the target video stream according to the client's QoS at run-time as quickly as possible. Experimental result shows that the proposed adaptation scheme can generate the target video stream about thirty times faster while keeping the quality degradation as less than 2% comparing to the target video stream that is totally dynamically generated, although the extra storages for the intermediate video streams are required.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2050-2059
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• 2000

In a video-on-Demand(VOD) service, a server has to return to he normal playback quickly at a certain new frame position after interactive operations such as jump or last playback. In this paper, we propose an efficient scheme to write a transmission schedule for a playback restart of a video stream at a new frame position after interactive operations. The proposed scheme is based on convergence characteristics, that is transmission schedules with different playback startup frame position in a video stream meet each other at some frame position. The scheme applies a bandwidth smoothing from a new frame position to a convergence position without considering all remaining frames of a video stream. And then the scheme transmits video dta according to the new schedule from the new frame position to the convergence position, and then transmits the remaining video data according to the reference schedule from the convergence position, and then transmits the remaining video data according to the reference schedule from the convergence position to the last frame position. In this paper, we showed that there existed the convergence position corresponding to nay frame position in a video stream through many experiments based on MPEG-1 bit trace data. With the convergence we reduced the computational overhead of a bandwidth smoothing, which was applied to find a new transmission schedule after interactive operations. Also, storage overhead is greatly reduced by storing pre-calculated schedule information up to the convergence position for each I frame position of a video stream with video data off-line. By saving information on a transmission schedule off-line along with the video data and searching the schedule corresponding to the specified restarting frame position, we expect the possibility of normal playback of a video stream with small tolerable playback startup delay.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.812-815
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• 2009

This paper discusses a searching method for special markers attached with persons in a surveillance video stream. The marker is a small plate with infrared LEDs, which is called a spatiotemporal marker because it shows a 2-D sequential pattern synchronized with video frames. The search is based on the motion vectors which is the same as one in video compression. The experiments using prototype markers show that the proposed method is practical. Though the method is applicable to a video stream independently, it can decrease total computation cost if motion vector analyses of a video compression and the proposed method is unified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.938-958
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• 2024

Unsupervised Video Object Segmentation (UVOS) is a highly challenging problem in computer vision as the annotation of the target object in the testing video is unknown at all. The main difficulty is to effectively handle the complicated and changeable motion state of the target object and the confusion of similar background objects in video sequence. In this paper, we propose a novel deep Dual-stream Co-enhanced Network (DC-Net) for UVOS via bidirectional motion cues refinement and multi-level feature aggregation, which can fully take advantage of motion cues and effectively integrate different level features to produce high-quality segmentation mask. DC-Net is a dual-stream architecture where the two streams are co-enhanced by each other. One is a motion stream with a Motion-cues Refine Module (MRM), which learns from bidirectional optical flow images and produces fine-grained and complete distinctive motion saliency map, and the other is an appearance stream with a Multi-level Feature Aggregation Module (MFAM) and a Context Attention Module (CAM) which are designed to integrate the different level features effectively. Specifically, the motion saliency map obtained by the motion stream is fused with each stage of the decoder in the appearance stream to improve the segmentation, and in turn the segmentation loss in the appearance stream feeds back into the motion stream to enhance the motion refinement. Experimental results on three datasets (Davis2016, VideoSD, SegTrack-v2) demonstrate that DC-Net has achieved comparable results with some state-of-the-art methods.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.2
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• pp.95-105
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• 2005

This paper proposes a new dynamic video adaptation scheme that could generate an adapted video stream customized to the requesting mobile device and current network status without repeated decode-encode cycles. In the proposed adaptation scheme, the characteristics of the video codec such as MPEG-1/-2/-4 are analyzed in advance focused on the relationships between the size and Quality of the encoded video stream, and they are stored in the proxy as a codec-dependent characteristic table. When a mobile device requests a video stream, it is dynamically decoded-encoded in the proxy with the highest quality to extract the contents-dependent attributes of the requested video stream. By comparing these attributes with codec-dependent characteristic table, the size and Quality of the requested video stream when being adapted to the target mobile device could be predicted. With this prediction, a version of adapted video stream, that meets the size constraints of mobile device while keeping the quality of encoded video stream as high as possible, could be selected without repeated decode-encode cycles. Experimental results show that the errors in our proposed scheme are less than 5% and produce an appropriate adapted video stream very quickly. It could be used t(1 build a proxy server for mobile devices that could quickly transcode the video streams widely spread in Internet which are encoded with various video codecs.

• Journal of Multimedia Information System
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• v.4 no.1
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• pp.19-26
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• 2017

The compression of video for both full HD and UHD requires the inclusion of extra vertical lines to every video frame, named as the DTV essential hidden area (DEHA), for the effective functioning of the MPEG-2/4/H encoder, stream, and decoder. However, while the encoding/decoding process is dependent on the DEHA, the DEHA is conventionally viewed as a redundancy in terms of channel utilization or storage efficiency. This paper proposes a block mode DEHA method to more effectively utilize the DEHA. Partitioning video block images and then evenly filling the representative DEHA macroblocks with the average DC coefficient of the active video macroblock can minimize the amount of DEHA data entering the compressed video stream. Theoretically, this process results in smaller DEHA data entering the video stream. Experimental testing of the proposed block mode DEHA method revealed a slight improvement in the quality of the active video. Outside of this technological improvement to video quality, the attractiveness of the proposed DEHA method is also heightened by the ease that it can be implemented with existing video encoders.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3668-3684
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• 2021

Video action recognition is widely used in video surveillance, behavior detection, human-computer interaction, medically assisted diagnosis and motion analysis. However, video action recognition can be disturbed by many factors, such as background, illumination and so on. Two-stream convolutional neural network uses the video spatial and temporal models to train separately, and performs fusion at the output end. The multi segment Two-Stream convolutional neural network model trains temporal and spatial information from the video to extract their feature and fuse them, then determine the category of video action. Google Xception model and the transfer learning is adopted in this paper, and the Xception model which trained on ImageNet is used as the initial weight. It greatly overcomes the problem of model underfitting caused by insufficient video behavior dataset, and it can effectively reduce the influence of various factors in the video. This way also greatly improves the accuracy and reduces the training time. What's more, to make up for the shortage of dataset, the kinetics400 dataset was used for pre-training, which greatly improved the accuracy of the model. In this applied research, through continuous efforts, the expected goal is basically achieved, and according to the study and research, the design of the original dual-flow model is improved.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.21-26
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• 2002

Video stream invoke a variety of traffic with the structure of compression algorithm and image complexity. Thus, it is difficult to allocate the resource on the both sides of sender and receiver, and playout on the Internet such as a packet switched network. Thus, in this paper we proposed video stream smoothing using multistream for the effective transmission of video stream. This method specifies the type of LDU(logical data unit) according to the type of original stream, and then makes a large number of streams as a fixed size, and transfers them. So, the proposed method can reduce the buffering time which occurs during the process of the smoothing and prefetch be robust to the jitter on network, as well. Consequently, it has the effective transmission characteristics of fully utilizing the clients bandwidth.