• The KIPS Transactions:PartD
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• v.14D no.7
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• pp.801-808
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• 2007

Video coding standards such as MPEG-2, MPEG-4, H.263, and H.264 transmit a compressed video data using wired/wireless communication line with limited bandwidth. Because highly compressed bit-streams is likely to fragile to error from channel noise, video is damaged by error. There have been many research works on error concealment techniques, which recover transmission errors at decoder side [1, 2]. We designed an error concealment technique for lost motion vectors of H.264 video coding. In this paper, we propose a Kalman filter based motion vector recovery scheme, and experimented with standard video sequences. The experimental results show that our scheme restores original motion vector with more precision of 0.91 - 1.12 on average over conventional H.264 decoding with no error recovery.

• The KIPS Transactions:PartC
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• v.18C no.6
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• pp.423-432
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• 2011

Recently, OpenFlow has been paid attention to as a fundamental technology which provides a function of virtualization and programmability in network. In Korea, deployment of OpenFlow networks in campuses and the interconnection between them through tunneling in layer 3 has been performed. However, the performance of the interconnected networks is decreased due to delay in IP layer. In this paper, we design and deploy nation-wide, not local, OpenFlow networks in a pure layer 2 environment over KREONET. After that, we do end-to-end Round-trip Time measurements and TCP/UDP performance tests in OpenFlow and normal networks, and do comparison and analysis on the test results. The results show that the nation-wide OpenFlow networks provide equal performance to normal networks except for the initial packet loss for UDP streaming. In regards to the performance decrease due to early UDP packet loss, we can mitigate it by implementing exceptional procedures in a controller which deal with the same continuous "Packet_in" events.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.6
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• pp.7-14
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• 2019

The automatic monitoring and detection of crowd behavior in the surveillance videos has obtained significant attention in the field of computer vision due to its vast applications such as security, safety and protection of assets etc. Also, the field of crowd analysis is growing upwards in the research community. For this purpose, it is very necessary to detect and analyze the crowd behavior. In this paper, we proposed a deep learning-based method which detects abnormal activities in surveillance cameras installed in a smart city. A fine-tuned VGG-16 model is trained on publicly available benchmark crowd dataset and is tested on real-time streaming. The CCTV camera captures the video stream, when abnormal activity is detected, an alert is generated and is sent to the nearest police station to take immediate action before further loss. We experimentally have proven that the proposed method outperforms over the existing state-of-the-art techniques.

• The Journal of the Acoustical Society of Korea
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• v.28 no.4
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• pp.392-400
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• 2009

In this paper, we propose an effective speech reinforcement technique base on ITU-T G.729A CS-ACELP codec under the near-end background noise environments. In general, since the intelligibility of the far-end speech for the near-end listener is significantly reduced under near-end noise environments, we require a far-end speech reinforcement approach to avoid this phenomena. In contrast to the conventional speech reinforcement algorithm, we reinforce the excitation signal of the codec's parameters received from the far-end speech signal based on the G.729A speech codec under various background noise environments. Specifically, we first estimate the excitation signal of ambient noise at the near-end through the encoder of the G.729A speech codec, reinforcing the excitation signal of the far-end speech transmitted from the far-end. we specially propose a novel approach to directly reinforce the excitation signal of far-end speech signal based on the decoder of the G.729A. The performance of the proposed algorithm is evaluated by the CCR (Comparison Category Rating) test of the method for subjective determination of transmission quality in ITU-T P.800 under various noise environments and shows better performances compared with conventional SNR Recovery methods.