• The Journal of Korea Robotics Society
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• v.3 no.3
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• pp.212-218
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• 2008

Collision avoidance is a fundamental and important task of an autonomous mobile robot for safe navigation in real environments with high uncertainty. Obstacles are classified into static and dynamic obstacles. It is difficult to avoid dynamic obstacles because the positions of dynamic obstacles are likely to change at any time. This paper proposes a scheme for vision-based avoidance of dynamic obstacles. This approach extracts object candidates that can be considered moving objects based on the labeling algorithm using depth information. Then it detects moving objects among object candidates using motion vectors. In case the motion vectors are not extracted, it can still detect the moving objects stably through their color information. A robot avoids the dynamic obstacle using the dynamic window approach (DWA) with the object path estimated from the information of the detected obstacles. The DWA is a well known technique for reactive collision avoidance. This paper also proposes an algorithm which autonomously registers the obstacle color. Therefore, a robot can navigate more safely and efficiently with the proposed scheme.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.365-368
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• 2001

This work describes a real-time method, based on motion energy detection, for detecting and tracking moving object in the consecutive image sequences. The motion of moving objects is detected by taking the difference of the two consecutive image frames. In addition an edge information of the current image is utilized in order to further increase the accuracy of detection. We can track the moving objects continuously by detecting the motion of objects from the sequence of image frames. A prototype system has been implemented using a TI TMS320C6201 EVM fixed-point DSP board, which can successfully track a moving human in real-time.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.63.5-63
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• 2001

In this paper, moving objects tracking and dynamic characteristic analysis are studied. Kohonen´s self-organizing neural network models are used for moving objects tracking and time delay neural networks are used for dynamic characteristic analysis. Instead of objects brightness, neuron projections by Kohonen Networks are used. The motion of target objects can be analyzed by using the differential neuron image between the two projections. The differential neuron image which is made by two consecutive neuron projections is used for center detection and moving objects tracking. The two differential neuron images which are made by three consecutive neuron projections are used for the moving trajectory estimation.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1044-1049
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• 2000

In detection of a scene change of the moving pictures which has massive information capacity, the temporal sampling method has faster searching speed than the sequential searching method for the whole moving pictures, yet employed searching algorithm and detection interval greatly affect searching time and searching precision. In this study, the whole moving pictures were primarily retrieved by the temporal sampling method. When there exist a scene change within the sampling interval, we suggested a fast searching algorithm using binary searching and derived an equation formula to determine optimal primary retrieval which can minimize computation, and showed the result of the experiment on MPEG moving pictures. The result of the experiment shows that the searching speed of the suggested algorithm is maximum 13 times faster than the one of he sequential searching method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.11
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• pp.2050-2056
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• 2008

A realization for moving object sensing and tracking system in two dimensional plane using stereo line CCDs and lighting source is presented in this paper. The system is realized that instead of processing camera images directly, two line CCD sensor and input line image is used to measure two dimensional distance by comparing the brightness on line CCDs. The algorithms are used the moving object sensing, path tracking and coordinate converting method. To ensure the effective detection of moving path, a detection algorithm to evaluate the reliability of each measured distance is developed. The realized system results are that the performance of moving object recognizing shows 5mm resolution, and enables to track a moving path of object per looms period.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.8
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• pp.661-669
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• 2017

This paper describes an MMTI(Maritime Moving Target Indicator) for periscope detection in airborne radar. Firstly, we analyze the characteristics of sea clutter, sea targets. Secondly, we study the differences between GMTI(Ground Moving Target Indicator) and MMTI. This paper proposes an optimal MMTI operating environment and method. We also suggest a signal processing algorithm using STAP(Space-Time Adaptive Processing) for detecting small RCS target moving low speed. The detection probability for moving target with MDV(Minimum Detectable Velocity) is simulated under various RCS and multi-channel system. Finally, we analyze the major performance for range, velocity and azimuth accuracy.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.77-81
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• 2014

This paper propose a method to detect moving object and locating in real time from video sequence. first the proposed method extract moving object by differencing two consecutive frames from the video sequence. If the interval between captured two frames is long, it cause to generate fake moving object as tail of the real moving object. secondly this paper proposed method to overcome this problem by using doppler effects and HSV color model. finally the object segmentation and locating is done by combining the result that obtained from steps above. The proposed method has 99.2% of detection rate in practical and also this method is comparatively speed than other similar methods those proposed in past. Since the complexity of the algorithm is directly affects to the speed of the system, the proposed method can be used as low complexity algorithm for real time moving object detection.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.263-269
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• 2010

This paper describes real-time detection and tracking of moving objects for unmanned visual surveillance. Using images obtained from the fixed camera it detects moving objects within the image and tracks them with displaying rectangle boxes enclosing the objects. Tracking method is implemented on an embedded system which consists of TI DSK645.5 kit and the FPGA board connected on the DSP kit. The DSP kit processes image processing algorithms for detection and tracking of moving objects. The FPGA board designed for image acquisition and display reads the image line-by-line and sends the image data to DSP processor, and also sends the processed data to VGA monitor by DMA data transfer. Experimental results show that the tracking of moving objects is working satisfactorily. The tracking speed is 30 frames/sec with 320x240 image resolution.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.281-284
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• 2003

In this study, we have proposed the method of moving area detection as the preprocessing step of moving object tracking system. First, we catch the two frames which are different at time in image sequence. We obtain the moving area by using their binary differential image. In differential image, the object area of previous and current frame is present. In the tracking system, the background is changed by camera motion. So, in this case we have to decide which moving area of object is current at time. We obtain the binary edge image of current frame by applying a threshold to the output of an edge detector. Then we performed logical AND operation between the edge image and differential image. As a result of this work moving area of object can be detected.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.289-298
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• 2009

Active Contour Model, that is, Snake algorithm is effective for detection and tracking the objects. However, this algorithm has some drawbacks; numerous parameters must be designed(weighting factors, iteration steps, etc.), a reasonable initialization must be available and moreover suffers from numerical instability. Therefore we propose a novel Energy Corrected Snake(ECS) algorithm which improved on external energy of Snake algorithm for detection and tracking the moving object more effectively. The proposed algorithm uses the difference image, getting when the object is moving. It copies four direction images from the difference image and performs the accumulating compute to erasing image noise, so that it gets external energy steadily. Then external energy united with contour that is computed by internal energy. Consequently we can detect and track the moving object more speedily and easily. To show the effectiveness of the proposed algorithm, we experiment on 3 situations. The experimental results showed that the proposed algorithm outperformed by 6$\sim$9% of detection rate and 6$\sim$11% of tracker detection rate compared with the Snake algorithm.