• Journal of the Korea Institute of Military Science and Technology
• /
• v.13 no.4
• /
• pp.561-568
• /
• 2010

Instrumentation radar in a test range has an important role to measure target's TSPI(time, space, position, information). It is well known that it tracks a target stably using a beacon mode. But it may fail to track a target in a certain region using a beacon mode. In this paper, we modeled a simple missile shape similar to ATCMS with two beacon antenna and analyzed an antenna radiation pattern using MLFMM(Multi Level Fast Multipole Method) method. Using the analyzed result of the radiation pattern of the antenna and the attitude data of target, we simulated beacon tracking performance of an instrumentation radar. As a result of simulation, we showed that an instrumentation radar may lose the target because it tracks a area of the beacon antenna pattern.

• ETRI Journal
• /
• v.42 no.1
• /
• pp.54-66
• /
• 2020

Nowadays, the broad availability of cameras and embedded systems makes the application of computer vision very promising as a supporting technology for intelligent transportation systems, particularly in the field of vehicle tracking. Although there are several existing trackers, the limitation of using low-cost cameras, besides the relatively low processing power in embedded systems, makes most of these trackers useless. For the tracker to work under those conditions, the video frame rate must be reduced to decrease the burden on computation. However, doing this will make the vehicle seem to move faster on the observer's side. This phenomenon is called the fast motion challenge. This paper proposes a tracker called dynamic swarm particle (DSP), which solves the challenge. The term particle refers to the particle filter, while the term swarm refers to particle swarm optimization (PSO). The fundamental concept of our method is to exploit the continuity of vehicle dynamic motions by creating dynamic models based on PSO. Based on the experiments, DSP achieves a precision of 0.896 and success rate of 0.755. These results are better than those obtained by several other benchmark trackers.

• ETRI Journal
• /
• v.29 no.6
• /
• pp.736-744
• /
• 2007

We propose a fast macroblock (MB) mode prediction and decision algorithm based on temporal correlation for P-slices in the H.264/AVC video standard. There are eight block types for temporal decorrelation, including SKIP mode based on rate-distortion (RD) optimization. This scheme gives rise to exhaustive computations (search) in the coding procedure. To overcome this problem, a thresholding method for fast inter mode decision using a MB tracking scheme to find the most correlated block and RD cost of the correlated block is suggested for early stop of the inter mode determination. We propose a two-step inter mode candidate selection method using statistical analysis. In the first step, a mode is selected based on the mode information of the co-located MB from the previous frame. Then, an adaptive thresholding scheme is applied using the RD cost of the most correlated MB. Secondly, additional candidate modes are considered to determine the best mode of the initial candidate modes that does not satisfy the designed thresholding rule. Comparative analysis shows that a speed-up factor of up to 70.59% is obtained when compared with the full mode search method with a negligible bit increment and a minimal loss of image quality.

• Journal of Veterinary Science
• /
• v.23 no.1
• /
• pp.17.1-17.10
• /
• 2022

Background: Inspection of livestock farms using surveillance cameras is emerging as a means of early detection of transboundary animal disease such as African swine fever (ASF). Object tracking, a developing technology derived from object detection aims to the consistent identification of individual objects in farms. Objectives: This study was conducted as a preliminary investigation for practical application to livestock farms. With the use of a high-performance artificial intelligence (AI)-based 3D depth camera, the aim is to establish a pathway for utilizing AI models to perform advanced object tracking. Methods: Multiple crossovers by two humans will be simulated to investigate the potential of object tracking. Inspection of consistent identification will be the evidence of object tracking after crossing over. Two AI models, a fast model and an accurate model, were tested and compared with regard to their object tracking performance in 3D. Finally, the recording of pig pen was also processed with aforementioned AI model to test the possibility of 3D object detection. Results: Both AI successfully processed and provided a 3D bounding box, identification number, and distance away from camera for each individual human. The accurate detection model had better evidence than the fast detection model on 3D object tracking and showed the potential application onto pigs as a livestock. Conclusions: Preparing a custom dataset to train AI models in an appropriate farm is required for proper 3D object detection to operate object tracking for pigs at an ideal level. This will allow the farm to smoothly transit traditional methods to ASF-preventing precision livestock farming.

• Journal of the Korean Institute of Telematics and Electronics
• /
• v.22 no.1
• /
• pp.75-85
• /
• 1985

We have studied on the 2-D moving target tracking algorithm satisfying a real-time hardware implementation requirement. In this paper, a fast algorithm is developed based on the operator formulation and the variational algorithm f 10] . Here, we use the directed search for the maximum of the cross-correlation in order to obtain an initial estimate for the variational algorithm and decompose the scene into 16 smaller subblocks and apply the variational algorithm to each subblock sequentially with a new moving area detection method. We call the algorithm subblock based recursive algorithm. Compared with (10) , the ratio of the computational savings obtained from the proposed algorithm is 7 on the average.

• Journal of Korea Multimedia Society
• /
• v.22 no.2
• /
• pp.311-321
• /
• 2019

Virtual reality applications in Head-Mounted Displays require high frame rates and low latency rendering techniques. Ray tracing offers many benefits, such as high-quality image generation, but has not been utilized due to lower performance than rasterization. But that can obtain good result combined with gaze-tracking technology and human visual system's operation principle. In this paper, we propose a method to optimize the foveated sampling map and to maintain the visual quality through the fast voronoi nearest interpolation. The proposed method further reduces the computational cost that has been improved by the previous foveated sampling. It also smoothes the voronoi boundary using adaptive sibson interpolation, which was not possible in real-time. As a result, the proposed method can render real-time high-quality images with low visual difference.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.9
• /
• pp.1825-1838
• /
• 2012

This paper proposes a fast face detection and tracking method which uses depth images as well as RGB images. It consists of the face detection procedure and the face tracking procedure. The face detection method basically uses an existing method, Adaboost, but it reduces the size of the search area by using the depth information and skin color. The proposed face tracking method uses a template matching technique and incorporates an early-termination scheme to reduce the execution time further. The results from implementing and experimenting the proposed methods showed that the proposed face detection method takes only about 39% of the execution time of the existing method. The proposed tracking method takes only 2.48ms per frame. For the exactness, the proposed detection method and previous method showed a same detection ratio but in the error ratio, which is about 0.66%, the proposed method showed considerably improved performance. In all the cases except a special one, the tracking error ratio is as low as about 1%. Therefore, we expect the proposed face detection and tracking methods can be used individually or in combined for many applications that need fast execution and exact detection or tracking.

• Journal of Institute of Control, Robotics and Systems
• /
• v.4 no.3
• /
• pp.349-359
• /
• 1998

A robot control scheme for specific application a line-tracking system is newly presented. To improve the performance of line-tracking, robot arm dynamics and torque constraints are incorporated into the control scheme. The tracking problem for the workpiece on a variable-speed conveyor is formulated as an optimal tracking problem with specific criteria. Dividing the conveyor speed into the nominal term and the perturbed term, a two-stage control strategy is employed to cope with the nonlinearity and uncertainty of the robot-conveyor system. Simulation results are given to verify good tracking performance with fast cycle time and high accuracy in a robotic workcell.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.18 no.12
• /
• pp.2961-2967
• /
• 2014

Nowadays, a private protection act has come into effect which demands for the protection of personal image information obtained by the CCTV. According to this act, the object out of interest has to be mosaicked such that it can not be identified before the image is sent to the investigation office. Meanwhile, the demand for digital videos obtained by CCTV is also increasing for digital forensic. Therefore, due to the two conflicting demands, the demand for a solution which can automatically mask an object in the CCTV video is increasing and related IT industry is expected to grow. The core technology in developing a target masking solution is the object tracking technique. In this paper, we propose an object tracking technique which suits for the application of CCTV video object masking as a postprocess. The proposed method simultaneously uses the motion and the color information to produce a stable tracking result. Furthermore, the proposed method is based on the centroid shifting method, which is a fast color based tracking method, and thus the overall tracking becomes fast.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.40 no.3
• /
• pp.127-134
• /
• 2003

In this paper, the speed control problem of moving camera is investigated for tracking the movement of the human-face. The camera system with pan-tilt mechanism sends an image to PC and PC sends back tracking coordinate to the camera. Then the camera tracks a human face in real time. The speed of the stepping motors for moving the camera must be controlled to the target region fast enough and smoothly, In this paper, a fuzzy logic controller is proposed for driving step motors. By creating driving acceleration and deceleration speed Profile, the speed of the motors is controled fast and smoothly. Experiments are performed to show the effectiveness of the proposed method.