• Journal of the Ergonomics Society of Korea
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• v.28 no.3
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• pp.49-54
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• 2009

Visual occlusion method is a visual demand measuring technique which uses periodic vision/occlusion cycle to simulate driving environment. It became one of the most popular techniques for the evaluation of in-vehicle interfaces due to its robustness and cost-effectiveness. However, it has a limitation in that the vision/occlusion cycle forces the user to use the IVIS at a predetermined pace, while a driver decides when to use the device on his/her own in actual driving. This paper proposes a user-driven visual occlusion method for measuring the visual demand of in-vehicle interfaces. An experiment was conducted to examine the visual demand of an in-vehicle interface prototype using both the existing (system-driven) occlusion method and the proposed (user-driven) one. Two in-vehicle tasks were evaluated: address input and radio tuning. The results showed that, for the radio tuning task, there were significant differences in total shutter open time and resumability ratio between the methods. The user-driven visual occlusion method not only allows a better representation of drivers' behavior, but it also seems to provide more information on the chunkability of a task.

• Journal of the Ergonomics Society of Korea
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• v.30 no.5
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• pp.589-595
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• 2011

Objective: This study compares two visual occlusion methods for the evaluation of in-vehicle interfaces. Background: Visual occlusion is a visual demand measuring technique which uses periodic vision/occlusion cycle to simulate a driving(or mobile) environment. It has been widely used for the evaluation of in-vehicle interfaces. There are two major implementation methods for this technique: (1) occlusion using PLATO(portable liquid crystal apparatus for tachistoscopic occlusion) goggles; (2) occlusion using a software application on a touchscreen device. Method: An experiment was conducted to examine the visual demand of an in-vehicle interface prototype using the goggle-based and the touchscreen-based occlusion methods. Address input and radio tuning tasks were evaluated in the experiment. Results: The results showed that, for the radio tuning task, there were no significant differences in total shutter open time and resumability ratio between the two occlusionconditions. However, it took longer for the participants to input addresses with the touchscreen-based occlusion. Conclusion & Application: The results suggest that touchscreen-based method could be used as an alternative to traditional, gogglebased visual occlusion especially in less demanding visual tasks such as radio tuning.

• 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 the Korea Society of Computer and Information
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• v.22 no.12
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• pp.55-61
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• 2017

In this paper, we propose a novel tracking method using target separation and detection that are based on discriminative correlation filter (DCF), which is studied a lot recently. 'Retainability' is one of the most important factor of tracking. There are some factors making retainability of tracking worse. Especially, fast movement and occlusion of a target frequently occur in image data, and when it happens, it would make target lost. As a result, the tracking cannot be retained. For maintaining a robust tracking, in this paper, separation of a target is used so that normal tracking is maintained even though some part of a target is occluded. The detection algorithm is executed and find new location of the target when the target gets out of tracking range due to occlusion of whole part of a target or fast movement speed of a target. A variety of experiments with various image data sets are conducted. The algorithm proposed in this paper showed better performance than other conventional algorithms when fast movement and occlusion of a target occur.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2108-2111
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• 2003

This paper introduces a new local feature extraction method and image matching method for the localization and classification of targets. Proposed method is based on the block-by-block projection associated with directional pattern of blocks. Each pattern has its own eigen-vertors called as CEBs(Classified Eigen-Blocks). Also proposed block-based image matching method is robust to translation and occlusion. Performance of proposed feature extraction and matching method is verified by the face localization and FLIR-vehicle-image classification test.

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.334-338
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• 2022

In visual simultaneous localization and mapping (vSLAM), the correct recognition of a place benefits in relocalization and improved map accuracy. However, its performance is significantly affected by the environmental conditions such as variation in light, viewpoints, seasons, and presence of dynamic objects. This research addresses the problem of feature occlusion caused by interference of dynamic objects leading to the poor performance of visual place recognition algorithm. To overcome the aforementioned problem, this research analyzes the role of scene semantics in correct detection of a place in challenging environments and presents a semantics aided visual place recognition method. Semantics being invariant to viewpoint changes and dynamic environment can improve the overall performance of the place matching method. The proposed method is evaluated on the two benchmark datasets with dynamic environment and seasonal changes. Experimental results show the improved performance of the visual place recognition method for vSLAM.

• 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.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.663-670
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• 2006

For virtual target to be displaying on real image realistically, it is essential to determine the location of the virtual object together with producing the occlusions area after registering two world. In this paper, we propose the new method to solve occlusions which happens during virtual target moves according to the simulated route on real image. For this purpose, we first construct three dimensional virtual world by DEM of experimental area and register CCD camera image on it by visual clues. Next, we also propose a method to solve the occlusion using snake and picking algorithm which can extract the three dimensional information of the position happening occlusion in the image and can use it when target moves that area. In the experiment, we proved the effectiveness of the proposed method in the environment which a partial occlusions happens.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.871-891
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• 2019

Visual tracking is a challenging task that needs learning an effective model to handle the changes of target appearance caused by factors such as pose variation, illumination change, occlusion and motion blur. In this paper, a novel tracking algorithm based on weighted local sub-space reconstruction error is presented. First, accounting for the appearance changes in the tracking process, a generative weight calculation method based on structural reconstruction error is proposed. Furthermore, a template update scheme of occlusion-aware is introduced, in which we reconstruct a new template instead of simply exploiting the best observation for template update. The effectiveness and feasibility of the proposed algorithm are verified by comparing it with some state-of-the-art algorithms quantitatively and qualitatively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2094-2112
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• 2019

Focusing on the issue that the conventional Kernel Correlation Filter (KCF) algorithm has poor performance in handling scale change and obscured objects, this paper proposes an anti-occlusion and scale adaptive tracking algorithm in the basis of KCF. The average Peak-to Correlation Energy and the peak value of correlation filtering response are used as the confidence indexes to determine whether the target is obscured. In the case of non-occlusion, we modify the searching scheme of the KCF. Instead of searching for a target with a fixed sample size, we search for the target area with multiple scales and then resize it into the sample size to compare with the learnt model. The scale factor with the maximum filter response is the best target scaling and is updated as the optimal scale for the following tracking. Once occlusion is detected, the model updating and scale updating are stopped. Experiments have been conducted on the OTB benchmark video sequences for compassion with other state-of-the-art tracking methods. The results demonstrate the proposed method can effectively improve the tracking success rate and the accuracy in the cases of scale change and occlusion, and meanwhile ensure a real-time performance.