• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.912-918
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• 1994

Poor speech intelligibility in an air traffic control room is frequently a result of many, quite different causes and occasionally leads to complaints of the controller personnel. The paper describes a sequence of successful tasks performed in a local control room. The initial measurements included an investigation of the background noise (caused by fans, air condition, computer and radar equipment) and performance checks of the electronic audio and communication equipment with respect to the audio transmission behavior. The spectral composition of the noise as well as the characteristics of the audio communication path between the controllers and the pilots(which showed a loss of spectral information in the audio band due to built-in notch filters for the suppression of control tones) required adaptations of the amplitude behavior of the amplifiers through user adjustable tone controls. The radar console fans, which contributed significantly to the overall noise floor of the room, underwent a substantial reconstruction by replacing the tight mounting with an elastic double suspension, reducing the noise level by 50%. Finally, a possible source of untimely fatigue of the controllers during their working hours has been found in strong spectral components of the noise above the audio band, radiated by numerous video monitors in the control through vibrating components excited by the line frequency of the video signal.

• ETRI Journal
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• v.36 no.4
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• pp.662-672
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• 2014

Human action recognition is used in areas such as surveillance, entertainment, and healthcare. This paper proposes a system to recognize both single and continuous human actions from monocular video sequences, based on 3D human modeling and cyclic hidden Markov models (CHMMs). First, for each frame in a monocular video sequence, the 3D coordinates of joints belonging to a human object, through actions of multiple cycles, are extracted using 3D human modeling techniques. The 3D coordinates are then converted into a set of geometrical relational features (GRFs) for dimensionality reduction and discrimination increase. For further dimensionality reduction, k-means clustering is applied to the GRFs to generate clustered feature vectors. These vectors are used to train CHMMs separately for different types of actions, based on the Baum-Welch re-estimation algorithm. For recognition of continuous actions that are concatenated from several distinct types of actions, a designed graphical model is used to systematically concatenate different separately trained CHMMs. The experimental results show the effective performance of our proposed system in both single and continuous action recognition problems.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.304-306
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• 1996

Endoscopy has become a crucial diagnostic and theraputic procedure in clinical areas. Over the past three years, we have developed a computerized system to record and store clinical data pertaining to endoscopic surgery of laparascopic cholesystectomy, peviscopic endometriosis, and surgical arthroscopy. In this study, we are developed computer system, which is composed of frame grabber, sound board, VCR control board, LAN card and EDMS(endoscopic data management software). Also, computer system has controled over peripheral instruments as a color video printer, video cassette recorder, and endoscopic input/output signals(image and doctor's speech). Also, we are developed one body system of camels control unit including an endoscopic miniature camera and light source. Our system offer unsurpassed image quality in terms of resolution and color fidelity. Digital endoscopic data management system is based on open architecture and a set of widely available industry standards, namely: windows 3.1 as a operating system, TCP/IP as a network protocol and a time sequence based database that handles both an image and drctor's speech synchronized with endoscopic image.

• Journal of information and communication convergence engineering
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• v.11 no.2
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• pp.124-131
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• 2013

Extraction and representation of postures and/or gestures from human activities in videos have been a focus of research in this area of action recognition. With various applications cropping up from different fields, this paper seeks to improve the performance of these action recognition machines by proposing a shape-based silhouette-edge descriptor for the human body. Information entropy, a method to measure the randomness of a sequence of symbols, is used to aid the selection of vital key postures from video frames. Morphological operations are applied to extract and stack edges to uniquely represent different actions shape-wise. To classify an action from a new input video, a Hausdorff distance measure is applied between the gallery representations and the query images formed from the proposed procedure. The method is tested on known public databases for its validation. An effective method of human action annotation and description has been effectively achieved.

• Journal of the Korea Computer Industry Society
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• v.2 no.7
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• pp.997-1014
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• 2001

In this paper, we propose a method for dynamic selection of unrestricted motion vector(UMV) or default prediction mode(DPM) in H.263 bit stream. For this, we use the error of compensated image and the magnitude of motion vector. In the proposed strategy, the UMV mode is dynamically applied in a frame according to average magnitude of motion vector and error of compensated image. This scheme has improved the quality of image compared to the fixed mode UMV or DPM only. Number of searching points are greatly reduced when comparing to UMV. The Proposed method is more profitable to long video sequences having camera movement locally.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.2
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• pp.118-123
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• 2011

Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.8
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• pp.1582-1588
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• 2007

According to recently presented QoS architecture by 3GPP, a traffic conditioner may be deployed to provide conformance of the negotiated QoS. In this paper, a real-time frame-layer rate control method which can be applied to the traffic conditioner of 3GPP is proposed. The proposed rate control method uses an efficient frame skipping algorithm method for low computational complexity, and performs bit allocation at the frame level to minimize the average distortion over an entire sequence as well as variations in distortion between frames. The proposed algorithm does not produce time delay from encoding, and is suitable for real-time low-complexity video encoder.

• Journal of the Korea Society of Computer and Information
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• v.14 no.8
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• pp.43-50
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• 2009

Text information in a digital video provides crucial information to acquire semantic information of the video. In the proposed method. text candidate regions are extracted from input sequence by using characteristics of stroke and text candidate regions are localized by using projection to produce text bounding boxes. Bounding boxes containing text regions are verified geometrically and each bounding box existing same location is tracked by calculating matching measure. which is defined as the mean of absolute difference between bounding boxes in the current frame and previous frames. Finally. text regions are enhanced using temporal redundancy of bounding boxes to produce final results. Experimental results for various videos show the validity of the proposed method.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1109-1116
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• 2023

Object tracking is used to track a goal in a video sequence by using coordinate information provided as annotation in the first frame of the video. In this paper, we propose a tracking algorithm that combines deep features and region inference modules to improve object tracking accuracy. In order to obtain sufficient object information, a convolution neural network was designed with a Siamese network structure. For object region inference, the region proposal network and overlapping confidence module were applied and used for tracking. The performance of the proposed tracking algorithm was evaluated using the Object Tracking Benchmark dataset, and it achieved 69.1% in the Success index and 89.3% in the Precision Metrics.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.2
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• pp.113-130
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• 2004

Most multimedia data contain text to emphasize the meaning of the data, to present additional explanations about the situation, or to translate different languages. But, the left makes it difficult to reuse the images, and distorts not only the original images but also their meanings. Accordingly, this paper proposes a support vector machines (SVMs) and spatiotemporal restoration-based approach for automatic text detection and removal in video sequences. Given two consecutive frames, first, text regions in the current frame are detected by an SVM-based texture classifier Second, two stages are performed for the restoration of the regions occluded by the detected text regions: temporal restoration in consecutive frames and spatial restoration in the current frame. Utilizing text motion and background difference, an input video sequence is classified and a different temporal restoration scheme is applied to the sequence. Such a combination of temporal restoration and spatial restoration shows great potential for automatic detection and removal of objects of interest in various kinds of video sequences, and is applicable to many applications such as translation of captions and replacement of indirect advertisements in videos.