• Proceedings of the Korean Institute of Communication Sciences Conference
• /
• 1987.04a
• /
• pp.56-59
• /
• 1987

In this study the moving information extraction techniques of moving objects are processed digital imaqe data by sampling three frames in a fixed-bacqround two-dimensional line sequence image the brightness of interframe are compared to extract difference image and difference image are two level formed and neighber averged From neigbber averaged image the parameters for recoqnition of the object are the number of contorur pixels, the number of vertex points and the distance between the vertex points Agtercomparing the same object the moving distance obtained from the coordinate which is constructed by the bit processing of the digital data and the moving velocity is obtained from the moving distance and the time interval between the first andsecond sampled frames.

• Proceedings of the KIEE Conference
• /
• 2001.11c
• /
• pp.365-368
• /
• 2001

This work describes a real-time method, based on motion energy detection, for detecting and tracking moving object in the consecutive image sequences. The motion of moving objects is detected by taking the difference of the two consecutive image frames. In addition an edge information of the current image is utilized in order to further increase the accuracy of detection. We can track the moving objects continuously by detecting the motion of objects from the sequence of image frames. A prototype system has been implemented using a TI TMS320C6201 EVM fixed-point DSP board, which can successfully track a moving human in real-time.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.3
• /
• pp.321-329
• /
• 1999

Moving Object segmentation extracts an interested moving object on a consecutive image frames, and has been used for factory automation, autonomous navigation, video surveillance, and VOP(Video Object Plane) detection in a MPEG-4 method. This paper proposes new segmentation method using difference images are calculated with three consecutive input image frames, and used to calculate both coarse object area(AI) and it's movement area(OI). An AI is extracted by removing background using background area projection(BAP). Missing parts in the AI is recovered with help of the OI. Boundary information of the OI confines missing parts of the object and gives inital curves for active contour optimization. The optimized contours in addition to the AI make the boundaries of the moving object. Experimental results of a fast moving object on a complex background scene are included.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2009.10a
• /
• pp.574-578
• /
• 2009

Real-time object detection for distinguishing a moving object of interests from the background image in still image or video image sequence is an essential step to a correct object tracking and recognition. Moving cast shadow can be misclassified as part of objects or moving objects because the shadow region is included in the moving object region after object segmentation. For this reason, an algorithm for shadow removal plays an important role in the results of accurate moving object detection and tracking systems. To handle with the problems, an accurate algorithm based on the features of moving object and shadow in color space is presented in this paper. Experimental results show that the proposed algorithm is effective to detect a moving object and to remove shadow in test video sequences.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.1
• /
• pp.69-76
• /
• 2010

The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.

• Proceedings of the IEEK Conference
• /
• 2002.07a
• /
• pp.200-203
• /
• 2002

We suggest a novel approach to the tracking of multiple moving objects in image sequence. The tracking of multiple moving objects include some complex problems(crossing (occluding), entering, disappearing, joining, and dividing) for objects identifying. Our method can settle these problems by optimization of a penalty function and movement prediction. It is executable in .eat time processing (more than 30 ㎐) because it is computed by only location data.

• Journal of the Korean Mathematical Society
• /
• v.45 no.2
• /
• pp.355-365
• /
• 2008

Let ${Y_i;-\infty<i<\infty}$ be a doubly infinite sequence of identically distributed and $\phi$-mixing random variables with zero means and finite variances and ${a_i;-\infty<i<\infty}$ an absolutely summable sequence of real numbers. In this paper, we prove the complete moment convergence of ${{\sum}_{k=1}^{n}\;{\sum}_{i=-\infty}^{\infty}\;a_{i+k}Y_i/n^{1/p};n\geq1}$ under some suitable conditions.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2009.01a
• /
• pp.673-677
• /
• 2009

In this paper, a transmitting process of a sequence of holograms describing 3D moving objects over the communicating wireless-network system is presented. A sequence of holograms involves holograms is transformed into a bit stream data, and then it is transmitted over the wireless LAN and Bluetooth. It is shown that applying this technique, holographic data of 3D moving object is transmitted in high quality and a relatively good reconstruction of holographic images is performed.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.42-42
• /
• 2000

This paper concerns an efficient aperiodic static crab walking algorithm for quadruped walking machine in rough terrain. In this algorithm, the requirements for forward stability margin and backward stability margin could be given differently in order to consider the slope of terrain and disturbances resulting from moving velocity. To restrict the searing regions for motion variables, such as moving distances until a leg is lifted or is placed, the standard leg transferring sequence is decided to be that of wave gaits. standard support pattern is also proposed that enables the quadruped to continue forward motion using the standard leg transferring sequence without falling into deadlock.

• Journal of Internet Computing and Services
• /
• v.10 no.5
• /
• pp.79-93
• /
• 2009

For developing the location based service which is individualized and specialized according to the characteristic of the users, the spatio-temporal pattern mining for extracting the meaningful and useful patterns among the various patterns of the mobile object on the spatio-temporal area is needed. Thus, in this paper, as the practical application toward the development of the location based service in which it is able to apply to the real life through the pattern mining from the huge historical data of mobile object, we are proposed STOMP(using Frequency of sequence and Weight) that is the new mining method for extracting the patterns with spatial and temporal constraint based on the problems of mining the optimal moving pattern which are defined in STOMP(F)[25]. Proposed method is the pattern mining method compositively using weighted value(weights) (a distance, the time, a cost, and etc) for our previous research(STOMP(F)[25]) that it uses only the pattern frequent occurrence. As to, it is the method determining the moving pattern in which the pattern frequent occurrence is above special threshold and the weight is most a little bit required among moving patterns of the object as the optimal path. And also, it can search the optimal path more accurate and faster than existing methods($A^*$, Dijkstra algorithm) or with only using pattern frequent occurrence due to less accesses to nodes by using the heuristic moving history.