• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.4
• /
• pp.159-166
• /
• 2016

In this paper, an active object tracking system basing on the pan/tilt-embedded stereo camera system is suggested and implemented. In the proposed system, once the face area of a target is detected from the input stereo image by using a YCbCr color model and phase-type correlation scheme and then, using this data as well as the geometric information of the tracking system, the distance and 3D information of the target are effectively extracted in real-time. Basing on these extracted data the pan/tilted-embedded stereo camera system is adaptively controlled and as a result, the proposed system can track the target adaptively under the various circumstance of the target. From some experiments using 480 frames of the test input stereo image, it is analyzed that a standard variation between the measured and computed the estimated target's height and an error ratio between the measured and computed 3D coordinate values of the target is also kept to be very low value of 1.03 and 1.18% on average, respectively. From these good experimental results a possibility of implementing a new real-time intelligent stereo target tracking and surveillance system using the proposed scheme is finally suggested.

• Progress in Medical Physics
• /
• v.24 no.3
• /
• pp.191-197
• /
• 2013

Upon radiation treatment, it is the important factor to monitor the patient's motion during radiation irradiated, since it can determine whether the treatment is successful. Thus, we have developed the system in which the patient's motion is monitored in real time and moving treatment position can be automatically corrected during radiation irradiation. We have developed the patient's position monitoring system in which the patient's position is three dimensionally identified by using two CCD cameras which are orthogonal located around the isocenter. This system uses the image pattern matching technique using a normalized cross-correlation method. We have developed the system in which trigger signal for beam on and off is generated by quantitatively analyzing the changes in a treatment position through delivery of the images taken from CCD cameras to the computer and the motor of moving couch can be controlled. This system was able to automatically correct a patient's position with the resolution of 0.5 mm or less.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.31 no.2C
• /
• pp.156-165
• /
• 2006

In this paper, a new intelligent moving target tracking and surveillance system basing on the pan/tilt-embedded stereo camera system is suggested and implemented. In the proposed system, once the face area of a target is detected from the input stereo image by using a YCbCr color model and phase-type correlation scheme and then, using this data as well as the geometric information of the tracking system, the distance and 3D information of the target are effectively extracted in real-time. Basing on these extracted data the pan/tilted-imbedded stereo camera system is adaptively controlled and as a result, the proposed system can track the target adaptively under the various circumstance of the target. From some experiments using 480 frames of the test input stereo image, it is analyzed that a standard variation between the measured and computed the estimated target's height and an error ratio between the measured and computed 3D coordinate values of the target is also kept to be very low value of 1.03 and 1.18$\%$ on average, respectively. From these good experimental results a possibility of implementing a new real-time intelligent stereo target tracking and surveillance system using the proposed scheme is finally suggested.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.13 no.4
• /
• pp.55-63
• /
• 2007

The Group Digital Surveillance System for Fishery Safety and Security (GDSS-F2S) is to provide the target tracking information and the target identification information in order to secure an huge aquaculture farm-field from a thief. The two information, however, is not enough to indict the thief due to the lack of lawful evidences for the crime actions. To overcome this problem, we consider the target image information as one of solutions after discussion with the effective countermeasure tools for the crime actions with scenario-based analysis according to the geological feature of aquaculture farm-field. To capture the real-time image for the trespassing targets in the aquaculture farm-field area, we developed the image capture system which is consists of ultra sensitive CCD(Charge-Coupled Device) camera with 0.0001 Lux and supplementary devices. As results from the field tests for GDSS-F2S with image capture system, the high definite images of the vehicle number plate and shape, person's actions and features are obtainable not only day time but also very dark night without moon light. Thus it is cleary known that the improved GDSS-F2S with image capture system can provide much enough lawful evidences for the crime actions of targets.

• Journal of Internet Computing and Services
• /
• v.25 no.1
• /
• pp.29-37
• /
• 2024

In this paper, we propose an efficient image processing system to detect and track the movement of specific objects such as patients. The proposed system extracts the outline area of an object from a binarized difference image by applying a thinning algorithm that enables more precise detection compared to previous algorithms and is advantageous for mixed-mode design. The binarization and thinning steps, which require a lot of computation, are designed based on RTL (Register Transfer Level) and replaced with optimized hardware blocks through logic circuit synthesis. The designed binarization and thinning block was synthesized into a logic circuit using the standard 180n CMOS library and its operation was verified through simulation. To compare software-based performance, performance analysis of binary and thinning operations was also performed by applying sample images with 640 × 360 resolution in a 32-bit FPGA embedded system environment. As a result of verification, it was confirmed that the mixed-mode design can improve the processing speed by 93.8% in the binary and thinning stages compared to the previous software-only processing speed. The proposed mixed-mode system for object recognition is expected to be able to efficiently monitor patient movements even in an edge computing environment where artificial intelligence networks are not applied.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.8
• /
• pp.1537-1545
• /
• 2016

In real time video sequence, object segmentation and tracking method are actively applied in various application tasks, such as surveillance system, mobile robots, augmented reality. This paper propose a robust object tracking method. The background models are constructed by learning the initial part of each video sequences. After that, the moving objects are detected via object segmentation by using background subtraction method. The region of detected objects are continuously tracked by using the HSV color histogram with particle filter. The proposed segmentation method is superior to average background model in term of moving object detection. In addition, the proposed tracking method provide a continuous tracking result even in the case that multiple objects are existed with similar color, and severe occlusion are occurred with multiple objects. The experiment results provided with 85.9 % of average object overlapping rate and 96.3% of average object tracking rate using two video sequences.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.4
• /
• pp.219-228
• /
• 2013

The surveillance imaging equipments are functioning to observe the shape of the target in real time or to measure its location precisely. The roles of such equipments are becoming more important in today's weapon systems.The aforementioned imaging equipments can be classified based on the modes of operations such as fixed, installed on cars, or composite of those. Also, according to different concepts of sensor operation, a separate type uses independent housing for each sensor whereas in a composite type a set of multiple sensors are housed into a unit altogether. The sensors in general have magnetism, thereby introducing the possible negative effects, particularly in the composite types, in locating the reference position, which is carried out by the digital compass. The use of shielding material/housing could be an option but results in increased weight and reduced portability, restricting its use in composite type equipments. As such, the objective of this paper is to study on how to reduce such magnetic effects on the position location. To do so, in the absence of magnetic shielding, a variety of sensor positions were first modeled. By combing the result with the fact that the functions of PAN & Tilt are used in the equipments, a new position location algorithm is proposed. The use of the new algorithm can automate the position location process as compared to the manual process of the existing approach. In the algorithm developed, twelve locations are measured in connection with both the azimuth and elevation angles in comparison to the six locations alone around the azimuth angle. As a result, it turns out that the measurement range has been widened but the measurement time reduced. Also, note that the effect of errors the operators may make during measurement could be reduced.

• Journal of Korea Society of Industrial Information Systems
• /
• v.16 no.4
• /
• pp.87-98
• /
• 2011

Detecting a moving object from videos and tracking it are basic and necessary preprocessing steps in many video systems like object recognition, context aware, and intelligent visual surveillance. In this paper, we propose a method that is able to detect a moving object quickly and accurately in a condition that background and light change in a real time. Furthermore, our system detects strongly an object in a condition that the target object is covered with other objects. For effective detection, effective Eigen-space and FCM are combined and employed, and a CONDENSATION algorithm is used to trace a detected object strongly. First, training data collected from a background image are linear-transformed using Principal Component Analysis (PCA). Second, an Eigen-background is organized from selected principal components having excellent discrimination ability on an object and a background. Next, an object is detected with FCM that uses a convolution result of the Eigen-vector of previous steps and the input image. Finally, an object is tracked by using coordinates of an detected object as an input value of condensation algorithm. Images including various moving objects in a same time are collected and used as training data to realize our system that is able to be adapted to change of light and background in a fixed camera. The result of test shows that the proposed method detects an object strongly in a condition having a change of light and a background, and partial movement of an object.

• Journal of Digital Convergence
• /
• v.15 no.2
• /
• pp.173-181
• /
• 2017

In this paper, we have studied tracking as a training stage of considering the position and the scale of a person given its previous position, scale, as well as next and forward image fraction. Unlike other learning methods, CNN is thereby learning combines both time and spatial features from the image for the two consecutive frames. We introduce multiple path ways in CNN to better fuse local and global information. A creative shift-variant CNN architecture is designed so as to alleviate the drift problem when the distracting objects are similar to the target in cluttered environment. Furthermore, we employ CNNs to estimate the scale through the accurate localization of some key points. These techniques are object-independent so that the proposed method can be applied to track other types of object. The capability of the tracker of handling complex situations is demonstrated in many testing sequences. The accuracy of the SVM classifier using the features learnt by the CNN is equivalent to the accuracy of the CNN. This fact confirms the importance of automatically optimized features. However, the computation time for the classification of a person using the convolutional neural network classifier is less than approximately 1/40 of the SVM computation time, regardless of the type of the used features.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.2
• /
• pp.149-162
• /
• 2005

This paper suggest a way to detect a specific wanted figure in public places such as subway stations and banks by comparing color face images extracted from the real time CCTV with the face images of designated specific figures. Assuming that the characteristic of the surveillance camera allows the face information in screens to change arbitrarily and to contain information on numerous faces, the accurate detection of the face area was focused. To solve this problem, the normalization work using subsampling with $20{\times}20$ pixels on arbitrary face images, which is based on the Perceptron Neural Network model suggested by R. Rosenblatt, created the effect of recogning the whole face. The optimal linear filter and the histogram shaper technique were employed to minimize the outside interference such as lightings and light. The addition operation of the egg-shaped masks was added to the pre-treatment process to minimize unnecessary work. The images finished with the pre-treatment process were divided into three reception fields and the information on the specific location of eyes, nose, and mouths was determined through the neural network. Furthermore, the precision of results was improved by constructing the three single-set network system with different initial values in a row.