• International Journal of Fuzzy Logic and Intelligent Systems
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• v.9 no.3
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• pp.232-237
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• 2009

Active safety vehicle devices are getting more attention recently. To prevent traffic accidents, the environment in front and even around the vehicle must be checked and monitored. In the present applications, mainly camera and radar based systems are used as sensing devices. Laser scanner, one of the sensing devices, has the advantage of obtaining accurate measurement of the distance and the geometric information about the objects in the field of view of the laser scanner. However, there is a problem that detecting object occluded by a foreground one is difficult. In this paper, criterions are proposed to manage this problem. Simulation is conducted by vehicle mounted the laser scanner and multiple-hypothesis algorithm tracks the candidate objects. We compare the running times as multi-hypothesis algorithm parameter varies.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.5
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• pp.537-542
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• 2011

A mirror neuron is a neuron fires both when an animal acts and when the animal observes the same action performed by another. We propose a method of 3D reconstruction for occluded object motion tracking like Mirror Neuron System to fire in hidden condition. For modeling system that intention recognition through fire effect like Mirror Neuron System, we calculate depth information using stereo image from a stereo camera and reconstruct three dimension data. Movement direction of object is estimated by optical flow with three-dimensional image data created by three dimension reconstruction. For three dimension reconstruction that enables tracing occluded part, first, picture data was get by stereo camera. Result of optical flow is made be robust to noise by the kalman filter estimation algorithm. Image data is saved as history from reconstructed three dimension image through motion tracking of object. When whole or some part of object is disappeared form stereo camera by other objects, it is restored to bring image date form history of saved past image and track motion of object.

• Journal of Korea Multimedia Society
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• v.15 no.4
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• pp.449-471
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• 2012

In intelligent surveillance systems, it is required to robustly track multiple people. Most of the previous studies adopted a Gaussian mixture model (GMM) for discriminating the object from the background. However, it has a weakness that its performance is affected by illumination variations and shadow regions can be merged with the object. And when two foreground objects overlap, the GMM method cannot correctly discriminate the occluded regions. To overcome these problems, we propose a new method of tracking and identifying multiple people. The proposed research is novel in the following three ways compared to previous research: First, the illuminative variations and shadow regions are reduced by an illumination normalization based on the median and inverse filtering of the L*a*b* image. Second, the multiple occluded and overlapped people are tracked by combining the GMM in the still image and the Lucas-Kanade-Tomasi (LKT) method in successive images. Third, with the proposed human tracking and the existing face detection & recognition methods, the tracked multiple people are successfully identified. The experimental results show that the proposed method could track and recognize multiple people with accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.712-717
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• 2013

In this paper, we propose a performance-enhanced object recognition by using nonlinear 3D correlator based on pixel restoration. In the proposed method, elemental images of the 3D target that are partially occluded by a foreground object are picked up and transformed into sub-images. By using the block-matching algorithm, the occluded target regions of each sub-image are estimated and removed. After that, the missing pixels in each sub-image are reestablished by using the pixel-restoration method. Finally, through the nonlinear cross-correlations between the reconstructed reference and the target plane images, the improved object recognition can be performed. To show the feasibility of the proposed method, some preliminary experiments are carried out and results are presented by comparing the conventional method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.23-29
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• 2010

Nowadays it is required to design intelligent visual surveillance systems which automatically detect abandoned objects in public places to strengthen the social safety. Already recognized abandoned objects can be occluded partially or fully by surrounding people in public places after the first recognition. To improve an essential recognition performance index PAT, the system should overcome the occlusion problems. In this research, a design scheme is newly proposed to construct the robust detection system which is comprised of multiple stages considering the occlusion problem. To show the feasibilities of the proposed system, the evaluation was tried for the prepared image streams including 6 various situations and the experimental results show 96% and 75% in PAT performance for intrusion and abandoning events, respectively. Finally in spite of full occlusions by multiple persons, the proposed system shows the capability to continuously recognize the abandoned object after complex occlusions disappear.

• Journal of the Optical Society of Korea
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• v.18 no.4
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• pp.388-394
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• 2014

In this paper, we propose a three-dimensional (3D) image correlator by use of computational integral imaging reconstruction based on the modified convolution property of periodic functions (CPPF) for recognition of partially occluded objects. In the proposed correlator, elemental images of the reference and target objects are picked up by a lenslet array, and subsequently are transformed to a sub-image array which contains different perspectives according to the viewing direction. The modified version of the CPPF is applied to the sub-images. This enables us to produce the plane sub-image arrays without the magnification and superimposition processes used in the conventional methods. With the modified CPPF and the sub-image arrays, we reconstruct the reference and target plane sub-image arrays according to the reconstruction plane. 3D object recognition is performed through cross-correlations between the reference and the target plane sub-image arrays. To show the feasibility of the proposed method, some preliminary experiments on the target objects are carried out and the results are presented. Experimental results reveal that the use of plane sub-image arrays enables us to improve the correlation performance, compared to the conventional method using the computational integral imaging reconstruction algorithm.

• Korean Journal of Computational Design and Engineering
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• v.18 no.5
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• pp.374-383
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• 2013

Various interaction techniques have been studied for providing the feeling of touch and improve immersion in augmented reality (AR) environments. Tangible AR interaction exploiting two types (product-type and pointer-type) of simple objects has earned great interest for cost-effective design evaluation of digital handheld products. When the sizes of markers attached to the objects are kept big to obtain better marker recognition, the pointer-type object frequently and significantly occludes the product-type object, which deteriorates natural visualization and level of immersion in an AR environment. In this paper, in order to overcome such problems, we propose tangible AR interaction using fingertip touch combined with small-sized markers. The proposed approach facilitates the use of convex polygons to recover the boundaries of AR markers which are partially occluded. It also properly enlarges the pattern area of each AR marker to reduce the sizes of AR markers without sacrificing the quality of marker detection. We empirically verified the quality of the proposed approach, and applied it in the process of design evaluation of digital products. From experimental results, we found that the approach is comparably accurate enough to be applied to the design evaluation process and tangible enough to provide a pseudo feeling of manipulating virtual products with human hands.

• Korean Journal of Computational Design and Engineering
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• v.8 no.2
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• pp.65-74
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• 2003

For virtual reality, virtual manufacturing system, or simulation based design, we need to visualize very large and complex 3D models which are comprising of very large number of polygons. To overcome the limited hardware performance and to attain smooth realtime visualization, there have been many researches about algorithms which reduce the number of polygons to be processed by graphics hardware. One of these algorithms, occlusion culling is a method of rejecting the objects which are not visible because they are occluded by other objects, and then passing only the visible objects to graphics hardware. Existing occlusion culling algorithms have some shortcomings such as the required long preprocessing time, the limitation of occluder shape, or the need for special hardware implementation. In this study, an efficient occlusion culling algorithm is proposed. The proposed algorithm reads and analyzes Z-buffer of graphics hardware using Microsoft DirectX, and then determines each object's visibility. This proposed algorithm can speed up visualization by reading Z-buffer using DirectX which can access hardware directly compared to OpenGL, by reading only the region to which each object is projected instead of reading the whole Z-Buffer, and the proposed algorithm can perform more exact visibility test by using simplified model instead of using bounding box. For evaluation, the proposed algorithm was applied to very large polygonal models. And smooth realtime visualization was attained.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.3
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• pp.127-136
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• 2003

In this paper, we propose a robust object tracking algorithm based on model and edge, using deformed template and Level-Set theory. The proposed algorithm can track objects in case of background variation, object flexibility and occlusions. First we design a new potential difference energy function(PDEF) composed of two terms including inter-region distance and edge values. This function is utilized to estimate and refine the object shape. The first step is to approximately estimate the shape and location of template object based on the assumption that the object changes its shape according to the affine transform. The second step is a refinement of the object shape to fit into the real object accurately, by using the potential energy map and the modified Level-Set speed function. The experimental results show that the proposed algorithm can track non-rigid objects under various environments, such as largely flexible objects, objects with large variation in the backgrounds, and occluded objects.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.1
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• pp.54-68
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• 2001

This paper proposes a new algorithm that detects and separates the occluding and occluded objects in a 2D image. An input image is represented by the attributed graph where a node corresponds to a surface and an arc connecting two nodes describes the adjacency of the nodes in the image. Each end of arc is weighted by relation value which tells the number of edges connected to the surface represented by the node in the opposite side of the arc. In attributed graph, homogeneous nodes pertained to a same object always construct one of three special patterns which can be simply classified by comparison of relation values of the arcs. The experimental results have shown that the proposed algorithm efficiently separates the objects overlapped arbitrarily, and that this approach of separating objects before matching operation reduces the matching time significantly by simplifying the matching problem of overlapped objects as the one of individual single object.