• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1711-1725
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• 2014

Multi-target tracking is the main purpose of many video surveillance applications. Recently, multi-target tracking based on the particle filter method has achieved robust results by using the data association process. However, this method requires many calculations and it is inadequate for real time applications, because the number of associations exponentially increases with the number of measurements and targets. In this paper, to reduce the computational cost of the data association process, we propose a novel multi-target tracking method that excludes particle samples in the overlapped predictive region between the target to track and marginal targets. Moreover, to resolve the occlusion problem, we define an occlusion mode with the normal dynamic mode. When the targets are occluded, the mode is switched to the occlusion mode and the samples are propagated by Gaussian noise without the sampling process of the particle filter. Experimental results demonstrate the robustness of the proposed multi-target tracking method even in occlusion.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.5
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• pp.505-510
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• 2009

In recent years the space where a robot works has been expanding to the human space unlike traditional industrial robots that work only at fixed positions apart from humans. A human in the recent situation may be the owner of a robot or the target in a robotic application. This paper deals with the latter case; when a robot vision system is employed to monitor humans for a surveillance application, each person in a scene needs to be identified. Humans, however, often move together, and occlusions between them occur frequently. Although this problem has not been seriously tackled in relevant literature, it brings difficulty into later image analysis steps such as tracking and scene understanding. In this paper, a probabilistic neural network is employed to learn the patterns of the best dividing position along the top pixels of an image region of partly occlude people. As this method uses only shape information from an image, it is simple and can be implemented in real time.

• Journal of Broadcast Engineering
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• v.16 no.4
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• pp.573-583
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• 2011

In this paper, we propose an occlusion compensation algorithm which is used for virtual view generation. In general, since occlusion region is recovered from neighboring pixels by taking the mean value or median value of neighbor pixels, the visual characteristics of a given image are not considered and consequently the accuracy of the compensated occlusion regions is not guaranteed. To solve these problem, we propose an algorithm that considers primary visual characteristics of a given image to compensate the occluded regions by using seam carving algorithm. In the proposed algorithm, we first use Sobel mask to obtain the edge map of a given image and then make it binary digit 0 or 1 and finally thinning process follows. Then, the energy patterns of original and thinned edge map obtained by the modified seam carving method are used to compensate the occlusion regions. Through experiments with many test images, we verify that the proposed algorithm performed better than conventional algorithms.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.132-140
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• 2016

In this paper, we propose boundary-preserving stereo matching method based on adaptive disparity adjustment using confidence region detection. To find the initial disparity map, we compute data cost using the color space (CIE Lab) combined with the gradient space and apply double cost aggregation. We perform left/right consistency checking to sort out the mismatched region. This consistency check typically fails for occluded and mismatched pixels. We mark a pixel in the left disparity map as "inconsistent", if the disparity value of its counterpart pixel differs by a value larger than one pixel. In order to distinguish errors caused by the disparity discontinuity, we first detect the confidence map using the Mean-shift segmentation in the initial disparity map. Using this confidence map, we then adjust the disparity map to reduce the errors in initial disparity map. Experimental results demonstrate that the proposed method produces higher quality disparity maps by successfully preserving disparity discontinuities compared to existing methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.35-49
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• 2004

This paper proposes a new glasses removal method from color frontal facial image to generate gray glassless facial image. The proposed method is based on recursive PCA reconstruction. For the generation of glassless images, the occluded region by glasses should be found, and a good reconstructed image to compensate with should be obtained. The recursive PCA reconstruction Provides us with both of them simultaneously, and finally produces glassless facial images. This paper shows the effectiveness of the proposed method by some experimental results. We believe that this method can be applied to removing other type of occlusion than the glasses with some modification and enhancing the performance of a face recognition system.

• Journal of Biomedical Engineering Research
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• v.24 no.5
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• pp.421-425
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• 2003

In this study the occlusion of dural-sac. the outer membrane of spinal cord in the lumbar region. was quantitatively analyzed using one motion segment finite element model. Occlusion was quantified by calculating cross sectional area change of dural-sac for different compressive impact duration (loading rate) due to bony fragment at the posterior wall of the cortical shell in vertebral body. Dural-sac was occluded most highly in the range of 8∼12 msec impact duration by the bony fragment intruding into the spinal canal. $\Delta$t = 400 msec case 4 % cross sectional area change was calculated. which is the same as the cross sectional area change under 6 kN of static compressive loading.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5436-5458
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• 2017

Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime has increased in the twenty-first century. As a new branch of AVSS, baggage detection has a wide area of security applications. Some of them are, detecting baggage in baggage restricted super shop, detecting unclaimed baggage in public space etc. However, in this paper, a detection & classification framework of baggage is proposed. Initially, background subtraction is performed instead of sliding window approach to speed up the system and HSI model is used to deal with different illumination conditions. Then, a model is introduced to overcome shadow effect. Then, occlusion of objects is detected using proposed mirroring algorithm to track individual objects. Extraction of rotational signal descriptor (SP-RSD-HOG) with support plane from Region of Interest (ROI) add rotation invariance nature in HOG. Finally, dynamic human body parameter setting approach enables the system to detect & classify single or multiple pieces of carried baggage even if some portions of human are absent. In baggage detection, a strong classifier is generated by boosting similarity measure based multi layer Support Vector Machine (SVM)s into HOG based SVM. This boosting technique has been used to deal with various texture patterns of baggage. Experimental results have discovered the system satisfactorily accurate and faster comparative to other alternatives.

• 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 Korea Multimedia Society
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• v.14 no.3
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• pp.378-391
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• 2011

In this paper, we suggest a novel method of selecting visual information based on bottom-up visual attention of human. We propose a new model that improve accuracy of detecting attention region by using depth information in addition to low-level spatial features such as color, lightness, orientation, form and temporal feature such as motion. Motion is important cue when we derive temporal saliency. But noise obtained during the input and computation process deteriorates accuracy of temporal saliency Our system exploited the result of psychological studies in order to remove the noise from motion information. Although typical systems get problems in determining the saliency if several salient regions are partially occluded and/or have almost equal saliency, our system is able to separate the regions with high accuracy. Spatiotemporally separated prominent regions in the first stage are prioritized using depth value one by one in the second stage. Experiment result shows that our system can describe the salient regions with higher accuracy than the previous approaches do.

• 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.