• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.773-777
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• 2016

In this paper, a novel multi-object tracking method to track an unknown number of objects is proposed. To handle multiple object states and uncertain observations efficiently, a probability hypothesis density (PHD) filter is adopted and modified. The PHD filter is capable of reducing false positives, managing object appearances and disappearances, and estimating the multiple object trajectories in a unified framework. Although the PHD filter is robust in cluttered environments, it is vulnerable to false negatives. For this reason, we propose to exploit local observations in an RFS of the observation model. Each local observation is generated by using an online trained object detector. The main purpose of the local observation is to deal with false negatives in the PHD filtering procedure. The experimental results demonstrated that the proposed method robustly tracked multiple objects under practical situations.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.16-22
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• 2011

This paper presents the multi-object tracking approach using the background difference and particle filtering by monte carlo sampling. We apply particle filters based on probabilistic importance sampling to multi-object independently. We formulate the object observation model by the histogram distribution using color information and the object dynaminc model for the object motion information. Our approach does not increase computational complexity and derive stable performance. We implement the whole Bayesian maximum likelihood framework and describes robust methods coping with the real-world object tracking situation by the observation and transition model.

• Journal of the Korean Society for Nondestructive Testing
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• v.22 no.5
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• pp.523-531
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• 2002

This study is concentrated on the development of high speed multi-object image processing algorithm in real time. Recently, the use of vision system is rapidly increasing in inspection and robot's position control. To apply the vision system, it is necessary to transform the physical coordinate of object into the image information acquired by CCD camera. Thus, to use the application of the vision system to the inspection and robot's position control in real time, we have to know the position of object in the image plane. Particularly, in case of rigid body using multi-cue to identify its shape, the each position of multi-cue must be calculated in an image plane at the same time. To solve these problems, the image processing algorithm on the position of multi-cue is developed.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.2
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• pp.127-136
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• 2016

The camera based object detection systems should satisfy the recognition performance as well as real-time constraints. Particularly, in safety-critical systems such as Autonomous Emergency Braking (AEB), the real-time constraints significantly affects the system performance. Recently, multi-core processors and system-on-chip technologies are widely used to accelerate the object detection algorithm by distributing computational loads. However, due to the advanced hardware, the complexity of system architecture is increased even though additional hardwares improve the real-time performance. The increased complexity also cause difficulty in migration of existing algorithms and development of new algorithms. In this paper, to improve real-time performance and design complexity, a task scheduling strategy is proposed for visual object tracking systems. The real-time performance of the vision algorithm is increased by applying pipelining to task scheduling in a multi-core processor. Finally, the proposed task scheduling algorithm is applied to crosswalk detection and tracking system to prove the effectiveness of the proposed strategy.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.35-41
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• 2015

When a video surveillance system tracks a specific object, it is very important to get quickly the information of the object through fast image processing. Usually one camera surveillance system for tracking the object made results in various problems such like occlusion, image noise during the tracking process. It makes difficulties on image based moving object tracking. Therefore, to overcome the difficulties the multi video surveillance system which installed several camera within interested area and looking the same object from multi angles of view could be considered as a solution. If multi cameras are used for tracking object, it is capable of making a decision having high accuracy in more wide space. This paper proposes a method of recognizing and tracking a specific object like a car using the homography in which multi cameras are installed at the crossroad.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.407-418
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• 2023

The Multi-Object Goal Visual Navigation(MultiOn) is a visual navigation task in which an agent must visit to multiple object goals in an unknown indoor environment in a given order. Existing models for the MultiOn task suffer from the limitation that they cannot utilize an integrated view of multimodal context because use only a unimodal context map. To overcome this limitation, in this paper, we propose a novel deep neural network-based agent model for MultiOn task. The proposed model, MCFMO, uses a multimodal context map, containing visual appearance features, semantic features of environmental objects, and goal object features. Moreover, the proposed model effectively fuses these three heterogeneous features into a global multimodal context map by using a point-wise convolutional neural network module. Lastly, the proposed model adopts an auxiliary task learning module to predict the observation status, goal direction and the goal distance, which can guide to learn the navigational policy efficiently. Conducting various quantitative and qualitative experiments using the Habitat-Matterport3D simulation environment and scene dataset, we demonstrate the superiority of the proposed model.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1126-1129
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• 2003

In this paper, we propose a multi-agent system for assisting those who need help in taking objects around him/her. One may imagine this kind of situation when a person is lying in bed and wishes to take an object on a distant table that cannot be reached only by stretching his/her hand. The proposed multi-agent system is composed of three main independent agents; a vision agent, a robot agent, and a pass agent. Once a human expresses his/her intention by pointing to a particular object using his/her hand and a finger, these agents cooperatively bring the object to him/her. Natural communication between a human and the multi-agent system is realized in this way. Performance of the proposed system is demonstrated in an experiment, in which a human intends to take one of the four objects on the floor and the three agents successfully cooperate to find out the object and to bring it to the human.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2005.11a
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• pp.209-212
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• 2005

This paper introduces interactive multi-view contents authoring system based on MPEG-4. The MPEG-4 standard, which aims to provide an object based audiovisual coding tool, has been developed to address the emerging needs from communications, interactive broadcasting as well as from mixed service models resulting from technological convergence. Due to the feature of object based coding, it has been considered that MPEG-4 is the most suitable for interactive broadcasting content production. This feature is suitable for creation of the content which provides multiple views of object or scene in interactive manner. In this paper, we categorize the multi-view visual content into two types: panoramic multi-view content and object multi-view content. And design and implementation of the authoring system for interactive multi-view contents is presented. We believe that the proposed method can be effectively used for further deployment of MPEG-4 content to various interactive applications.

• Journal of the Semiconductor & Display Technology
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• v.14 no.3
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• pp.67-72
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• 2015

This paper proposes an object tracking system based on keypoints using homography in mobile environments. The proposed system is based on markerless tracking, and there are four modules which are recognition, tracking, detecting and learning module. Recognition module detects and identifies an object to be matched on current frame correspond to the database using LSH through SURF, and then this module generates a standard object information. Tracking module tracks an object using homography information that generate by being matched on the learned object keypoints to the current object keypoints. Then update the window included the object for defining object's pose. Detecting module finds out the object based on having the best possible knowledge available among the learned objects information, when the system fails to track. The experimental results show that the proposed system is able to recognize and track objects with updating object's pose for the use of mobile platform.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.4
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• pp.669-686
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• 1997

In this research, an object-oriented Petri net model for representing a flexible process plan is proposed, which is hierarchically multi-faceted for supporting planning, scheduling, and shop floor control functions. The multi-faceted process plan model consists of the following: a) an object model which represents on object-oriented data model, b) a static model which represents a process flow model with process alternatives, and c) a dynamic model which represents a process activity model with resources alternatives, of a flexible process plan. Petri nets allow the static and the dynamic process plan models to be represented in a unified formalism with an ease of model transformation. The multi-faceted process plan model suggested in this paper, is illustrated with a prismatic port in comprehensive detail.