• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3159-3176
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• 2014

This work presents an analysis of the buffer features of an access router, especially the size, the impact on delay and the packet loss rate. In particular, we study how these features can affect the Quality of Service (QoS) of multimedia applications when generating traffic bursts in local networks. First, we show how in a typical SME (Small and Medium Enterprise) network in which several multimedia flows (VoIP, videoconferencing and video surveillance) share access, the upgrade of the bandwidth of the internal network may cause the appearance of a significant amount of packet loss caused by buffer overflow. Secondly, the study shows that the bursty nature of the traffic in some applications traffic (video surveillance) may impair their QoS and that of other services (VoIP and videoconferencing), especially when a certain number of bursts overlap. Various tests have been developed with the aim of characterizing the problems that may appear when network capacity is increased in these scenarios. In some cases, especially when applications generating bursty traffic are present, increasing the network speed may lead to a deterioration in the quality. It has been found that the cause of this quality degradation is buffer overflow, which depends on the bandwidth relationship between the access and the internal networks. Besides, it has been necessary to describe the packet loss distribution by means of a histogram since, although most of the communications present good QoS results, a few of them have worse outcomes. Finally, in order to complete the study we present the MOS results for VoIP calculated from the delay and packet loss rate.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.2
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• pp.95-100
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• 2013

Smart surveillance, is the use of automatic video analysis technologies in video surveillance applications. We present a robust object tracking method using pan-tilt-zoom camera for intelligent surveillance System, As the result of the experiment using 78 vehicle, the success rate of the tracking for moving object & non-moving object werw 97.4% and 91%. and 84.6%. the success rate o PTZ control for license plate image.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.2
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• pp.65-70
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• 2008

Smart surveillance, is the use of automatic video analysis technologies in video surveillance applications. We present a robust object tracking method using pan-tilt-zoom camera for intelligent surveillance System, As the result of the experiment using 78 vehicle, the success rate of the tracking for moving object & non-moving object werw 97.4% and 91%. and 84.6%. the success rate o PTZ control for license plate image.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.165-168
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• 2019

The advancement in technology has led to the enormous use of multimedia applications. The video/image recorded by such applications provides critical information that can be used as a visual evidence. However, owing to the adequacy in using different editing tools, it is susceptible to malicious alterations. Thus, the reliability or the integrity of the visual information should be verified before using it as an evidence. In this paper, we propose an integrity verification method for the surveillance system using randomized hashing. The integrity value of the surveillance data is generated using the randomized hashing and elliptic curve cryptography (ECC), which is used later for the validation. The experimental results obtained from the embedded accident data recorder (ADR) system shows that the proposed method is very efficient and provides a high level of security.

• Journal of Ecology and Environment
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• v.42 no.2
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• pp.85-89
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• 2018

Observations and video documentation of interactions between animals living in dens, cavities, and other enclosed spaces are difficult, but they play an important role in field biology, ecology, and conservation. For example, bird parents visiting nests and feeding their nestlings may provide crucial information for testing of ecological hypotheses and may easily attract attention of participants of citizen-science ecological and conservation projects. Because of the nest concealment of cavity-nesting birds, their behaviors in the nest can only be studied by using video surveillance. Professional wildlife surveillance systems are extremely expensive. Here, we describe an inexpensive video setup that can be constructed with relatively little effort and is more affordable than any previously described system. We anticipate that the relatively low cost of about 250 USD for a battery-operated system is an important feature for citizen-science type of projects and for applications in heavily populated areas where the potential for theft and vandalism may be high. Based on our experiences, we provide methodological advice on practical aspects of using this system in the field for ecological research on birds. We highlight the low cost, easiness of construction, and potential availability to a large number of observers taking part in wildlife monitoring projects, and we offer technical help to participants of such research projects.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1960-1963
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• 2003

For applications such as video surveillance and human computer interface, we propose an efficiently integrated method to detect and track faces. Various visual cues are combined to the algorithm: motion, skin color, global appearance and facial pattern detection. The ICA (Independent Component Analysis)-SVM (Support Vector Machine based pattern detection is performed on the candidate region extracted by motion, color and global appearance information. Simultaneous execution of detection and short-term tracking also increases the rate and accuracy of detection. Experimental results show that our detection rate is 91％ with very few false alarms running at about 4 frames per second for 640 by 480 pixel images on a Pentium IV 1㎓.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.169-189
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• 2015

Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of video frames to form a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed to form a temporal pattern in a sequence of video frames, representing temporal relation between various object's primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1711-1725
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• 2014

Multi-target tracking is the main purpose of many video surveillance applications. Recently, multi-target tracking based on the particle filter method has achieved robust results by using the data association process. However, this method requires many calculations and it is inadequate for real time applications, because the number of associations exponentially increases with the number of measurements and targets. In this paper, to reduce the computational cost of the data association process, we propose a novel multi-target tracking method that excludes particle samples in the overlapped predictive region between the target to track and marginal targets. Moreover, to resolve the occlusion problem, we define an occlusion mode with the normal dynamic mode. When the targets are occluded, the mode is switched to the occlusion mode and the samples are propagated by Gaussian noise without the sampling process of the particle filter. Experimental results demonstrate the robustness of the proposed multi-target tracking method even in occlusion.

• Journal of the Semiconductor & Display Technology
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• v.7 no.3
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• pp.45-49
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• 2008

Face detection and tracking technology on video sequence has developed indebted to commercialization of teleconference, telecommunication, front stage of surveillance system using face recognition, and video-phone applications. Complex background, color distortion by luminance effect and condition of luminance has hindered face recognition system. In this paper, we have proceeded to research of face recognition on video sequence. We extracted facial area using luminance and chrominance component on $YC_bC_r$ color space. After extracting facial area, we have developed the face recognition system applied to our improved algorithm that combined PCA and LDA. Our proposed algorithm has shown 92% recognition rate which is more accurate performance than previous methods that are applied to PCA, or combined PCA and LDA.

• The Journal of the Korea Contents Association
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• v.14 no.4
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• pp.9-18
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• 2014

Recently, many researches have been focused on multi-view video applications and services such as wireless video surveillance networks, wireless video sensor networks and wireless mobile video. In multi-view video signal processing, to exploit the strong correlation between images acquired by different cameras plays great role in developing a core technique of multi-view video coding. This paper proposes an adaptive multi-view video interpolation technique which is applicable for multi-view distributed video coding without requiring any cooperation amongst the cameras. The proposed algorithm estimates the non-linear moving blocks and employs disparity compensated view prediction, and then fills in the unreliable blocks. Through computer simulations, it is shown that the proposed method outperforms the conventional methods.