• Proceedings of the KIEE Conference
• /
• 1998.07g
• /
• pp.2357-2359
• /
• 1998

This paper proposes trajectory tracking of the moving object based on one camera vision system. And, this system proposes a method which robot manipulator grips moving object and predicts coordinate of moving objcet. The trajectory tracking and position coordinate are computed by vision data acquired to camera. Robot manipulator tracks and grips moving object by vision data. The proposed vision systems use a algorithm to do real-time processing.

• ETRI Journal
• /
• v.45 no.5
• /
• pp.811-821
• /
• 2023

We propose an adaptive unmanned aerial vehicle (UAV)-assisted object recognition algorithm for urban surveillance scenarios. For UAV-assisted surveillance, UAVs are equipped with learning-based object recognition models and can collect surveillance image data. However, owing to the limitations of UAVs regarding power and computational resources, adaptive control must be performed accordingly. Therefore, we introduce a self-adaptive control strategy to maximize the time-averaged recognition performance subject to stability through a formulation based on Lyapunov optimization. Results from performance evaluations on real-world data demonstrate that the proposed algorithm achieves the desired performance improvements.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.6
• /
• pp.562-570
• /
• 2012

This paper proposes a robust object tracking algorithm using object features and on-line learning based particle filter for mobile robots. Mobile robots with a side-view camera have problems as camera jitter, illumination change, object shape variation and occlusion in variety environments. In order to overcome these problems, color histogram and HOG descriptor are fused for efficient representation of an object. Particle filter is used for robust object tracking with on-line learning method IPCA in non-linear environment. The validity of the proposed algorithm is revealed via experiments with DBs acquired in variety environment. The experiments show that the accuracy performance of particle filter using combined color and shape information associated with online learning (92.4 %) is more robust than that of particle filter using only color information (71.1 %) or particle filter using shape and color information without on-line learning (90.3 %).

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.10
• /
• pp.958-964
• /
• 2015

In this paper, an object recognition method based on the depth information from the RGB-D camera, Xtion, is proposed for an indoor mobile robot. First, the RANdom SAmple Consensus (RANSAC) algorithm is applied to the point cloud obtained from the RGB-D camera to detect and remove the floor points. Next, the removed point cloud is classified by the k-means clustering method as each object's point cloud, and the normal vector of each point is obtained by using the k-d tree search. The obtained normal vectors are classified by the trained multi-layer perceptron as 18 classes and used as features for object recognition. To distinguish an object from another object, the similarity between them is measured by using Levenshtein distance. To verify the effectiveness and feasibility of the proposed object recognition method, the experiments are carried out with several similar boxes.

• Journal of Institute of Control, Robotics and Systems
• /
• v.12 no.8
• /
• pp.746-751
• /
• 2006

In this paper, we present tracking algorithm for the indoor moving object. We research passive method using a camera and image processing. It had been researched to use dynamic based estimators, such as Kalman Filter, Extended Kalman Filter and Particle Filter for tracking moving object. These algorithm have a good performance on real-time tracking, but they have a limit. If the shape of object is changed or object is located on complex background, they will fail to track them. This problem will need the complicated image processing algorithm. Finally, a large algorithm is made from integration of dynamic based estimator and image processing algorithm. For eliminating this inefficiency problem, image based estimator, Mean-shift Algorithm is suggested. This algorithm is implemented by color histogram. In other words, it decide coordinate of object's center from using probability density of histogram in image. Although shape is changed, this is not disturbed by complex background and can track object. This paper shows the results in real camera system, and decides 3D coordinate using the data from mean-shift algorithm and relationship of real frame and camera frame.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.8
• /
• pp.766-772
• /
• 2015

This paper proposes an object detection method based on motion estimation using background subtraction in the fisheye images obtained through omni-directional camera mounted on the vehicle. Recently, most of the vehicles installed with rear camera as a standard option, as well as various camera systems for safety. However, differently from the conventional object detection using the image obtained from the camera, the embedded system installed in the vehicle is difficult to apply a complicated algorithm because of its inherent low processing performance. In general, the embedded system needs system-dependent algorithm because it has lower processing performance than the computer. In this paper, the location of object is estimated from the information of object's motion obtained by applying a background subtraction method which compares the previous frames with the current ones. The real-time detection performance of the proposed method for object detection is verified experimentally on embedded board by comparing the proposed algorithm with the object detection based on LKOF (Lucas-Kanade optical flow).

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.11
• /
• pp.989-995
• /
• 2015

Due to the recent increase of the importance and demand of security services, the importance of a surveillance monitor system that makes an automatic security system possible is increasing. As the market for surveillance monitor systems is growing, price competitiveness is becoming important. As a result of this trend, surveillance monitor systems based on an embedded system are widely used. In this paper, an object detection algorithm based on an embedded system for a surveillance monitor system is introduced. To apply the object detection algorithm to the embedded system, the most important issue is the efficient use of resources, such as memory and processors. Therefore, designing an appropriate algorithm considering the limit of resources is required. The proposed algorithm uses two background models; therefore, the embedded system is designed to have two independent processors. One processor checks the sub-background models for if there are any changes with high update frequency, and another processor makes the main background model, which is used for object detection. In this way, a background model will be made with images that have no objects to detect and improve the object detection performance. The object detection algorithm utilizes one-dimensional histogram distribution, which makes the detection faster. The proposed object detection algorithm works fast and accurately even in a low-priced embedded system.

• 제어로봇시스템학회:학술대회논문집
• /
• 1990.10b
• /
• pp.933-938
• /
• 1990

A method of recognizing 2-dimensional polygonal object is proposed by using a concept of generalized incremental circle transform. The generalized incremental circle transform, which maps boundaries of an object into a circular disc, represents efficiently the shape of the boundaries that are obtained from digirized binary images of the objects. It is proved that the generalized incremental circle transform of an object is invariant to object translation, rotation, and size, and can be used as feature information for recognizing two dimensional polygonal object efficiently.

• Journal of Korea Society of Industrial Information Systems
• /
• v.12 no.4
• /
• pp.32-40
• /
• 2007

A mobile device accesses large-sized data in object-based IP storage as an object unit over IP network. It relies heavily on a WLAN device, which has been known as one of the major power consumers. This paper designs and implements low-power object-based IP storage for mobile devices using an efficient WLAN power control. The proposed WLAN power control exploits prefetch buffer to maximize the idleness for incoming network traffic and controls available WLAN power modes to minimize the power consumption. Our experimental results reveal that the proposed WLAN control can save the total power consumption in a PXA270-based mobile device about 9% while playing the multimedia contents through an object-based IP storage device

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.518-521
• /
• 1995

In this paper, a learning control problem is formulated for cooperating multiple-robot manipulators with uncertain system parameters. The commonly held object is also assumed to be unknown and the multiple-robots themselfs experience uncertain operating conditions such as link parameters, viscous friction parameters, suctions, actuator bias, and etc. Under these conditions, the learning controllers designed for learning of uncertain parameters and robot control inputs for multiple-robot systems are shown to drive the multiple-robot manipulators to follow the desired Cartesian trajectory with the desired internal forces to the unknown object.