• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.421-422
• /
• 2006

Using the still image through the camera reports which the moving object tracking system. Moving object direction detected to compare the two difference images. And base block set at moving object. Matching area set current difference image. The edge image of prior frame and current frame implement the moving object tracking system to block matching.

• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.9 s.174
• /
• pp.77-84
• /
• 2005

In most vision applications, we are frequently confronted with determining the position of object continuously. Generally, intertwined processes ire needed for target tracking, composed with tracking and control process. Each of these processes can be studied independently. In case of actual implementation we must consider the interaction between them to achieve robust performance. In this paper, the robust real time visual tracking in complex background is considered. A common approach to increase robustness of a tracking system is to use known geometric models (CAD model etc.) or to attach the marker. In case an object has arbitrary shape or it is difficult to attach the marker to object, we present a method to track the target easily as we set up the color and shape for a part of object previously. Robust detection can be achieved by integrating voting-based visual cues. Kalman filter is used to estimate the motion of moving object in 3D space, and this algorithm is tested in a pan/tilt robot system. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• The Journal of the Korea Contents Association
• /
• v.12 no.3
• /
• pp.44-54
• /
• 2012

In wireless sensor networks, a moving object tracking scheme is one of core technologies for real applications such as environment monitering and enemy moving tracking in military areas. However, no works have been carried out on processing the failure of object tracking in sparse sensor networks with holes. Therefore, the energy consumption in the existing schemes significantly increases due to plenty of failures of moving object tracking. To overcome this problem, we propose a novel moving object tracking scheme based on polynomial regression prediction in sparse sensor networks. The proposed scheme activates the minimum sensor nodes by predicting the trajectory of an object based on polynomial regression analysis. Moreover, in the case of the failure of moving object tracking, it just activates only the boundary nodes of a hole for failure recovery. By doing so, the proposed scheme reduces the energy consumption and ensures the high accuracy for object tracking in the sensor network with holes. To show the superiority of our proposed scheme, we compare it with the existing scheme. Our experimental results show that our proposed scheme reduces about 47% energy consumption for object tracking over the existing scheme and achieves about 91% accuracy of object tracking even in sensor networks with holes.

• Proceedings of the IEEK Conference
• /
• 1999.06a
• /
• pp.615-618
• /
• 1999

In this paper, a object detection and tracking algorithm is presented which exhibits robust properties for image sequences with complex background. The proposed algorithm is composed of three parts: moving object detection, object tracking, and motion analysis. The moving object detection algorithm is implemented using a temporal median background method which is suitable for real-time applications. In the motion analysis, we propose a new technique for removing a temporal clutter, such as a swaying plant or a light reflection of a background object. In addition, we design a multiple vehicle tracking system based on Kalman filtering. Computer simulation of the proposed scheme shows its robustness for MPEG-7 test image sequences.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.17 no.4
• /
• pp.106-115
• /
• 2003

In crossing stereo camera system as a security equipment system, a new algorithm for the tracking method of the moving object and the measurement method of distance of the object from cameras to moving object is proposed. The stereo input images are matched by a pixel for the moving object extraction, then the moving elements obtained from an adaptive threshold's value for an error correction and the moving object area extracted by noise eliminative algorithm. In addition to, the moving object traced by control pan/tilt of cameras using a coordinate value of the moving object, and the measurement method of distance to moving object by using special features of the crossing stereo camera system is proposed. From the experimental results, the proposed algorithm was found to be able to measure of the distance and trace for moving object in a stereo security camera system. The error rate of a distance measurement be existed within 6%. Moreover in implementation the proposed algorithm, the stereo security system, the automatic moving robot system and the stereo remote control system will be applied practical application.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2007.10a
• /
• pp.277-280
• /
• 2007

A moving object tracking algorithm for image searching based on specific color detection is proposed in this paper. That is preprocessed for a luminance variation and noise cancellation to be robust system. The motion tracking is used the difference between input image and reference image in R, G, B each channels for a moving image. The proposed method is enhanced to 15% fast in comparison with the contour tracking method and the matching method, and stable.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10b
• /
• pp.573-574
• /
• 1992

We developed a recognition and tracking system of moving objects. The system consists of one CCD video camera, two DC motors in horizontal and vertical axles with encoders, pluse width modulation(PWM) driving unit, 16 bit NEC 9801 microcomputer, and their interfaces. The recognition and tracking system is able to recognize shape and size of a moving object and is able to track the object within a certain range of errors. This paper presents the brief introduction of the recognition and tracking system developed in our laboratory.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2009.10a
• /
• pp.574-578
• /
• 2009

Real-time object detection for distinguishing a moving object of interests from the background image in still image or video image sequence is an essential step to a correct object tracking and recognition. Moving cast shadow can be misclassified as part of objects or moving objects because the shadow region is included in the moving object region after object segmentation. For this reason, an algorithm for shadow removal plays an important role in the results of accurate moving object detection and tracking systems. To handle with the problems, an accurate algorithm based on the features of moving object and shadow in color space is presented in this paper. Experimental results show that the proposed algorithm is effective to detect a moving object and to remove shadow in test video sequences.

• International Journal of Control, Automation, and Systems
• /
• v.1 no.4
• /
• pp.495-502
• /
• 2003

This paper focuses on the implementation of an efficient tracking method of a moving object using optimal representative blocks by way of a pan-tilt camera. The key idea is derived from the fact that when the image size of a moving object is shrunk in an image frame according to the distance between the mobile robot camera and the object in motion, the tracking performance of a moving object can be improved by reducing the size of representative blocks according to the object image size. Motion estimations using Edge Detection (ED) and Block-Matching Algorithm (BMA) are regularly employed to track objects by vision sensors. However, these methods often neglect the real-time vision data since these schemes suffer from heavy computational load. In this paper, a representative block able to significantly reduce the amount of data to be computed, is defined and optimized by changing the size of representative blocks according to the size of the object in the image frame in order to improve tracking performance. The proposed algorithm is verified experimentally by using a two degree-of- freedom active camera mounted on a mobile robot.

• Proceedings of the Korea Institute of Convergence Signal Processing
• /
• 2003.06a
• /
• pp.82-85
• /
• 2003

This paper proposes a preprocessing method for tracking aircraft's take-off and lading. The method uses accumulative difference image technique for segmenting the object from the background, and obtains the centroid of the object exactly using centroid method. Then the moving object is analyzed and represented with the information such as feature point, velocity, and distance. A simulation result reveals that the proposed algorithm has good performance in segmenting and tracking the aircraft.