• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.2
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• pp.31-37
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• 2007

This paper presents a ballistic lead-computation method which utilizes automatic video tracking, tracking assistance and roll uncoupling. The method is able to improve the firing accuracy of army fighting vehicles such as main battle tanks. In the experiment, the efficiency of the proposed method is evaluated by an error analysis in real operating environment. The proposed method has been applied to the fire control system of a military vehicle and proved through the development test of the vehicle.

• Journal of the Semiconductor & Display Technology
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• v.22 no.3
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• pp.161-165
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• 2023

One of the difficult works in an autonomous driving system is detecting road lanes or objects in the road boundaries. Detecting and tracking a vehicle is able to play an important role on providing important information in the framework of advanced driver assistance systems such as identifying road traffic conditions and crime situations. This paper proposes a vehicle detection scheme based on deep learning to classify and tracking vehicles in a complex and diverse environment. We use the modified YOLO as the object detector and polynomial regression as object tracker in the driving video. With the experimental results, using YOLO model as deep learning model, it is possible to quickly and accurately perform robust vehicle tracking in various environments, compared to the traditional method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4489-4501
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• 2014

We propose a novel lane detection method based on classification in image sequences. Both structural and statistical features of the extracted bright shape are applied to the neural network for finding correct lane marks. The features used in this paper are shown to have strong discriminating power to locate correct traffic lanes. The traffic lanes detected in the current frame is also used to estimate the traffic lane if the lane detection fails in the next frame. The proposed method is fast enough to apply for real-time systems; the average processing time is less than 2msec. Also the scheme of the local illumination compensation allows robust lane detection at nighttime. Therefore, this method can be widely used in intelligence transportation systems such as driver assistance, lane change assistance, lane departure warning and autonomous vehicles.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.434-436
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• 2006

In a visual driver-assistance system, separating moving objects from fixed objects are an important problem to maintain multiple hypothesis for the state. Color and edge-based tracker can often be "distracted" causing them to track the wrong object. Many researchers have dealt with this problem by using multiple features, as it is unlikely that all will be distracted at the same time. In this paper, we improve the accuracy and robustness of real-time tracking by combining a color histogram feature with a brightness of Optical Flow-based feature under a Sequential Monte Carlo framework. And it is also excepted from Tracking as time goes on, reducing density by Adaptive Particles Number in case of the fixed object. This new framework makes two main contributions. The one is about the prediction framework which separating moving objects from fixed objects and the other is about measurement framework to get a information from the visual data under a partial occlusion.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.5 s.311
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• pp.1-9
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• 2006

Because most applications of vision-based object tracking demonstrate satisfactory operations only under very constrained environments that have simplifying assumptions or specific visual attributes, these approaches can't track target objects for the highly variable, unstructured, and dynamic environments like a traffic scene. An adaptive fusion framework is essential that takes advantage of the richness of visual information such as color, appearance shape and so on, especially at cluttered and dynamically changing scenes with partial occlusion[1]. This paper develops a particle filter based adaptive fusion framework and improves the robustness and adaptation of this framework by adding a new distinctive visual attribute, an image feature descriptor using SIFT (Scale Invariant Feature Transform)[2] and adding an automatic teaming scheme of the SIFT feature library according to viewpoint, illumination, and background change. The proposed algorithm is applied to track various traffic objects like vehicles, pedestrians, and bikes in a driver assistance system as an important component of the Intelligent Transportation System.

• Journal of Korea Multimedia Society
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• v.9 no.5
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• pp.529-541
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• 2006

In this paper, we propose a semi-automatic segmentation method which can be used to generate video object plane (VOP) for object based coding scheme and multimedia authoring environment. Semi-automatic segmentation can be considered as a user-assisted segmentation technique. A user can initially mark objects of interest around the object boundaries and then the selected objects are continuously separated from the un selected areas through time evolution in the image sequences. The proposed segmentation method consists of two processing steps: partially manual intra-frame segmentation and fully automatic inter-frame segmentation. The intra-frame segmentation incorporates user-assistance to define the meaningful complete visual object of interest to be segmentation and decides precise object boundary. The inter-frame segmentation involves boundary and region tracking to obtain temporal coherence of moving object based on the object boundary information of previous frame. The proposed method shows stable and efficient results that could be suitable for many digital video applications such as multimedia contents authoring, content based coding and indexing. Based on this result, we have developed objects based video editing system with several convenient editing functions.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.718-724
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• 2013

In today's automotive industry, there exist several systems that help drivers reduce the possibility of accidents, such as the ADAS (Advanced Driver Assistance System). The ADAS helps drivers make correct and quick decisions during dangerous situations. This study analyzed the performance of the IMM (Interacting Multiple Model) method based on multiple Kalman filters using the data acquired from a driving simulator. An IMM algorithm is developed to identify the current discrete state of neighboring vehicles using the sensor data and the vehicle dynamics. In particular, the driving modes of the neighboring vehicles are classified by the cruising and maneuvering modes, and the transition between the states is modeled using a Markovian switching coefficient. The performance of the IMM algorithm is analyzed through realistic simulations where a target vehicle executes sudden lane change or acceleration maneuver.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1036-1042
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• 2013

In this paper, an elderly assistance system is developed based on Xenomai, a real-time development framework cooperating with the Linux kernel. A Kinect sensor is used to recognize the behavior of the elderly and A-star search algorithm is implemented to find the shortest path to the person. The mobile robot also generates a trajectory using a digital convolution operator which is based on a Bezier curve for smooth driving. In order to follow the generated trajectory within the control period, we developed real-time tasks and compared the performance of the tracking trajectory with that of non real-time tasks. The real-time task has a better result on following the trajectory within the physical constraints which means that it is more appropriate to apply to an elderly assistant system.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.1
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• pp.40-49
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• 2007

In a visual driver-assistance system, separating moving objects from fixed objects are an important problem to maintain multiple hypothesis for the state. Color and edge-based tracker can often be 'distracted' causing them to track the wrong object. Many researchers have dealt with this problem by using multiple features, as it is unlikely that all will be distracted at the same time. In this paper, we improve the accuracy and robustness of real-time tracking by combining a color histogram feature with a brightness of Optical Flow-based feature under a Sequential Monte Carlo framework. And it is also excepted from Tracking as time goes on, reducing density by Adaptive Particles Number in case of the fixed object. This new framework makes two main contributions. The one is about the prediction framework which separating moving objects from fixed objects and the other is about measurement framework to get a information from the visual data under a partial occlusion.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.8
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• pp.837-848
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• 2014

This paper describes the development of a walking assistive robot for effective self-rehabilitation for elderly people facing an inconvenience in walking. The main features of the developed robot are enhanced safety and mobility using the baby walker and electric wheelchair mechanisms and an accurate walking tracking control algorithm using potentiometers and stereo cameras. Specifically, a pelvis supporter is designed to prevent the user from falling down and reduce the burden on their legs, and electric motors are used for easy locomotion with low effort. Next, the walking intention and direction of the user are automatically recognized by using potentiometers attached at the pelvis supporter so that the robot can track the user, and the rapidity and accuracy of the tracking were increased by applying a lower-body motion analysis algorithm with stereo cameras. Finally, the user-tracking performance of the developed robot was experimentally verified through stepwise walking assistance experiments.