• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2057-2060
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• 2016

Range-based motion estimation of a moving object by using a camera is proposed. Whereas the existing results constrain the motion of an object for the motion estimation of an object, the constraints on the motion is relieved in the proposed method in that a more generally moving object motion can be handled. To this end, a nonlinear observer is designed based on the relative dynamics between the object and camera so that the object velocity and the unknown camera velocity can be estimated. Stability analysis and simulation results for the moving object are provided to show the effectiveness of the proposed method.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1570-1578
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• 2000

Object motion is an important feature of content in video sequences. By now, various methods to exact feature about the object motion have been reported[1,2]. However they are not suitable to index video using the motion, since a lot of bits and complex indexing parameters are needed for the indexing [3,4] In this paper, we propose object motion map which could provide efficient indexing method for object motion. The proposed object motion map has both global and local motion information during an object is moving. Furthermore, it requires small bit of memory for the indexing. to evaluate performance of proposed indexing technique, experiments are performed with video database consisting of MPEG-1 video sequence in MPEG-7 test set.

• Journal of Korea Multimedia Society
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• v.17 no.8
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• pp.915-922
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• 2014

We propose an adaptive zoom motion estimation method where a picture is divided into two areas based on the distance information with a depth camera : the one is object area and the other is background area. In the proposed method, the zoom motion is only applied to the object area except the background area. Further, the block size of motion estimation for the object area is set to smaller than that of background area. This adaptive zoom motion estimation method can be reduced at the complexity of motion estimation and can be improved at the motion estimation performance by reducing the block size of the object area in comparison with the conventional zoom motion estimation method. Based on the simulation results, the proposed method is compared with the conventional methods in terms of motion estimation accuracy and computational complexity.

• Journal of Sensor Science and Technology
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• v.5 no.4
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• pp.57-70
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• 1996

Motion can be estimated continuously from each sensor through the analysis of the instantaneous states of an object. This paper is aimed to introduce a method to estimate the general 3D motion of a planar object from the instantaneous states of an object using multi-sensor data fusion. The instantaneous states of an object is estimated using the linear feedback estimation algorithm. The motion estimated from each sensor is fused to provide more accurate and reliable information about the motion of an unknown planar object. We present a fusion algorithm which combines averaging and deciding. With the assumption that the motion is smooth, the approach can handle the data sequences from multiple sensors with different sampling times. Simulation results show proposed algorithm is advantageous in terms of accuracy, speed, and versatility.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.679-684
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• 1993

This article presents an approach to estimate the general 3D motion of a polyhedral object using multiple, sensory data some of which may not provide sufficient information for the estimation of object motion. Motion can be estimated continuously from each sensor through the analysis of the instantaneous state of an object. We have introduced a method based on Moore-Penrose pseudo-inverse theory to estimate the instantaneous state of an object. A linear feedback estimation algorithm is discussed to estimate the object 3D motion. Then, the motion estimated from each sensor is fused to provide more accurate and reliable information about the motion of an unknown object. The techniques of multisensor data fusion can be categorized into three methods: averaging, decision, and guiding. We present a fusion algorithm which combines averaging and decision.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.87-98
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• 1994

Object-oriented analysis-synthesis coding subdivides each image of a sequence into moving objects and compensates the motion of each object. Thus it can reconstruct real motion better than conventional motion-compensated coding techniques at very-low-bit-rates. It uses a mapping parameter technique for estimating motion information of each object. Since a mapping parameter technique uses gradient operators it is sensitive to redundant details and noise. To accurately determine mapping parameters, we propose a new analysis method using integral projections for estimation of gradient values. Also to reconstruct correctly the local motion the proposed algorithm divides an image into segmented objects each of which having uniform motion information while the conventional one assumes a large object having the same motion information. Computer simulation results with several test sequences show that the proposed image analysis method in object-oriented analysis-synthesis coding shows better performance than the conventional one.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.87-95
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• 2003

This paper proposes an object detection algorithm using the Multiresolution Motion Estimation(MRME) in wavelet d야main. A existing motion estimation method has characteristics of motion estimation but it requires having computation. Motion estimation in higher resolution used the motion vector of the lower resolution with the MRME that has parent-child relationship on wavelet coefficients. This method reduces the search area of motion estimation in higher resolution and computational complexity. The computational complexity of the proposed method is about 40% of the existing method using 3-level Set Partitioning in Hierarchical Trees(SPIHT) wavelet transform. The experimental results with the proposed method showed about 11% decrease of Mean Absolute Difference(MAD) and gains able to precise tracking of object.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2733-2737
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• 2014

Motion detection according to the movement and the change area detection method according to the background difference and the motion history image for use in a motion estimation technique using a real-time image, the motion detection method according to the optical flow, the back-projection of the histogram of the object to track for motion tracking At the heart of MeanShift center point of the object and the object to track, while used, the size, and the like due to the motion tracking algorithm CamShift, Kalman filter to track with direction. In this paper, we implemented the motion detection algorithm based on color and shape information of the object and verify.

• Journal of information and communication convergence engineering
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• v.2 no.1
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• pp.52-57
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• 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.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.2
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• pp.15-19
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• 2008

In this paper, we propose a new algorithm of image depth detection using motion estimation and object tracking. In industry, robots are used for automobile, conveyer system, etc. But, these have much necessary time. Thus, in this paper, we develop the efficient method of image depth detection based on motion estimation and object tracking.