• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
• /
• pp.1944-1947
• /
• 2003

In this paper, we propose a dynamic-based compression system by creating mosaic background and transmitting the change information. A dynamic mosaic of the background is progressively integrated in a single image using the camera motion information. For the camera motion estimation, we calculate affine motion parameters for each frame sequentially with respect to its previous frame. The camera motion is robustly estimated on the background by discriminating between background and foreground regions. The modified block-based motion estimation is used to separate the back-ground region.

• 한국정보통신학회:학술대회논문집
• /
• 한국해양정보통신학회 2003년도 추계종합학술대회
• /
• pp.741-744
• /
• 2003

본 논문은 모자이크 배경을 생성하고 변화되는 정보만을 전송함으로서 동적기반 압축시스템을 제안한다. 동적 모자이크 배경은 카메라 움직임 정보를 이용하여 단일영상으로 점진적으로 통합된다. 카메라 움직임 예측을 위해 각각의 영상들과 이전영상과의 원근투영 매개변수를 순차적으로 계산하였다. 카메라 움직임은 배경영역과 전경영역에서 식별함으로서 배경상에서 강건하게 계측된다. 수정된 블록기반 움직임계측은 배경영역을 분리하는데 이용되었다.

• Journal of information and communication convergence engineering
• /
• 제2권1호
• /
• pp.52-57
• /
• 2004

In this paper, we propose a panorama-based object tracking scheme for wide-view surveillance systems that can detect and track moving objects with a pan-tilt camera. A dynamic mosaic of the background is progressively integrated in a single image using the camera motion information. For the camera motion estimation, we calculate affine motion parameters for each frame sequentially with respect to its previous frame. The camera motion is robustly estimated on the background by discriminating between background and foreground regions. The modified block-based motion estimation is used to separate the background region. Each moving object is segmented by image subtraction from the mosaic background. The proposed tracking system has demonstrated good performance for several test video sequences.

• 한국멀티미디어학회논문지
• /
• 제12권12호
• /
• pp.1728-1735
• /
• 2009

Human action tracking plays an important place in human-computer-interaction, human action tracking is a challenging task because of the exponentially increased computational complexity in terms of the degrees of freedom of the object and the severe image ambiguities incurred by frequent self-occlusions. In this paper, we will propose a novel method to track human action, in our technique, a dynamic background estimation algorithm will be applied firstly. Based on the estimated background, we then extract the human object from the video sequence, and the skeletonization method and Hough transform method will be used to detect the main structure of human body and each part rotation angle. The calculated rotation angles will be used to control a crane in the port, thus we can just control the container crane by using signalman body. And the experimental results can show that our proposed method can get a preferable result than the conventional methods such as: MIT, JPF or MFMC.

• 한국철도학회:학술대회논문집
• /
• 한국철도학회 2008년도 추계학술대회 논문집
• /
• pp.883-890
• /
• 2008

Dynamic instability of the railway bridge can cause track irregularities and be directly connected with unstable passenger comfort and derailment of the train. Europe and Japan had studied on this problem for a long time since 1960's, Results of those studies are reflected in recent Eurocode and the bridge design code of Japanese railway bridges. However, even though some traffic safety requirements were applied in Kyoung-bu highspeed railway by French engineers, we experienced many difficulties from insufficient information of criteria background and application procedures. In the present study, various studies will be presented for the estimation of dynamic performance will be proposed and procedures and methods of estimation will be shown.

• 대한임베디드공학회논문지
• /
• 제11권5호
• /
• pp.277-285
• /
• 2016

Moving object detection system has been an emerging research field in various advanced driver assistance systems (ADAS) and surveillance system. In this paper, we propose two optical flow based moving object detection methods at dynamic scenes. Both proposed methods consist of three successive steps; pre-processing, foreground segmentation, and post-processing steps. Two proposed methods have the same pre-processing and post-processing steps, but different foreground segmentation step. Pre-processing calculates mainly optical flow map of which each pixel has the amplitude of motion vector. Dense optical flows are estimated by using Farneback technique, and the amplitude of the motion normalized into the range from 0 to 255 is assigned to each pixel of optical flow map. In the foreground segmentation step, moving object and background are classified by using the optical flow map. Here, we proposed two algorithms. One is Gaussian mixture model (GMM) based background subtraction, which is applied on optical map. Another is adaptive thresholding based foreground segmentation, which classifies each pixel into object and background by updating threshold value column by column. Through the simulations, we show that both optical flow based methods can achieve good enough object detection performances in dynamic scenes.

• 드라이브 ㆍ 컨트롤
• /
• 제14권3호
• /
• pp.40-49
• /
• 2017

This study presents an online estimation of an excavator's rotational inertia by using recursive least square with forgetting. It is difficult to measure rotational inertia in real systems. Against this background, online estimation of rotational inertia is essential for improving safety and automation of construction equipment such as excavators because changes in inertial parameter impact dynamic characteristics. Regarding an excavator, rotational inertia for swing motion may change significantly according to working posture and digging conditions. Hence, rotational inertia estimation by predicting swing motion is critical for enhancing working safety and automation. Swing velocity and damping coefficient were used for rotational inertia estimation in this study. Updating rules are proposed for enhancing convergence performance by using the damping coefficient and forgetting factors. The proposed estimation algorithm uses three forgetting factors to estimate time-varying rotational inertia, damping coefficient, and torque with different variation rates. Rotational inertia in a typical working scenario was considered for reasonable performance evaluation. Three simulations were conducted by considering several digging conditions. Presented estimation results reveal the proposed estimation scheme is effective for estimating varying rotational inertia of the excavator.

• The Journal of the Acoustical Society of Korea
• /
• 제25권2E호
• /
• pp.69-73
• /
• 2006

In this paper, we propose a robust music audio fingerprinting system for automatic music retrieval. The fingerprint feature is extracted from the long-term dynamic modulation spectrum (LDMS) estimation in the perceptual compressed domain. The major advantage of this feature is its significant robustness against severe background noise from the street and cars. Further the fast searching is performed by looking up hash table with 32-bit hash values. The hash value bits are quantized from the logarithmic scale modulation frequency coefficients. Experiments illustrate that the LDMS fingerprint has advantages of high scalability, robustness and small fingerprint size. Moreover, the performance is improved remarkably under the severe recording-noise conditions compared with other power spectrum-based robust fingerprints.

• Structural Engineering and Mechanics
• /
• 제53권3호
• /
• pp.607-623
• /
• 2015

The wind-induced dynamic response of large-span flexible structures includes two important components-background response and resonant response. However, it is difficult to separate the two components in time-domain. To solve the problem, a relational expression of wavelet packet coefficients and power spectrum is derived based on the principles of digital signal processing and the theories of wavelet packet analysis. Further, a new approach is proposed for separation of the background response from the resonant response. Then a numerical example of frequency detection is provided to test the accuracy and the spectral resolution of the proposed approach. In the engineering example, the approach is applied to compute the power spectra of the wind-induced response of a large-span roof structure, and the accuracy of spectral estimation for stochastic signals is verified. The numerical results indicate that the proposed approach is efficient and accurate with high spectral resolution, so it is applicable for power spectral computation of various response signals of structures induced by the wind. Moreover, the background and the resonant response time histories are separated successfully using the proposed approach, which is sufficiently proved by detailed verifications. Therefore, the proposed approach is a powerful tool for the verification of the existing frequency-domain formulations.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제5권12호
• /
• pp.2355-2373
• /
• 2011

Vision-based vehicle detector systems are becoming increasingly important in ITS applications. Real-time operation, robustness, precision, accurate estimation of traffic parameters, and ease of setup are important features to be considered in developing such systems. Further, accurate vehicle detection is difficult in varied complex traffic environments. These environments include changes in weather as well as challenging traffic conditions, such as shadow effects and jams. To meet real-time requirements, the proposed system first applies a color background to extract moving objects, which are then tracked by considering their relative distances and directions. To achieve robustness and precision, the color background is regularly updated by the proposed algorithm to overcome luminance variations. This paper also proposes a scheme of feedback compensation to resolve background convergence errors, which occur when vehicles temporarily park on the roadside while the background image is being converged. Next, vehicle occlusion is resolved using the proposed prior split approach and through reasoning for rule-based tracking. This approach can automatically detect straight lanes. Following this step, trajectories are applied to derive traffic parameters; finally, to facilitate easy setup, we propose a means to automate the setting of the system parameters. Experimental results show that the system can operate well under various complex traffic conditions in real time.