• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.1
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• pp.61-69
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• 2002

Active stereo vision system allows us to localize a target object by passing only the features of small disparities without heavy computation for identifying the target. This simple method, however, is not applicable to the situations where a distracting background is included or the target and other objects are located on the zero disparity area simultaneously To alleviate these problems, we combined filtering with foveation which employs high resolution in the center of the visual field and suppresses the periphery which is usually less interesting. We adopted an image pyramid or log-polar transformation for foveated imaging representation. We also extracted the stereo disparity of the target by using projection to keep the stereo disparity small during tracking. Our experiments show that log-polar transformation is superior to either an image pyramid or traditional method in separating a target from the distracting background and fairly enhances the tracking performance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.209-214
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• 2018

The subway, which is an urban railway, is the core of public transportation. Urban railways are always exposed to serious problems such as theft, crime and terrorism, as many passengers use them. Especially, due to the nature of urban railway environment, the scope of surveillance is widely dispersed and the range of surveillance target is rapidly increasing. Therefore, it is difficult to perform comprehensive management by passive surveillance like existing CCTV. In this paper, we propose the implementation, design method and object recognition algorithm for intelligent object recognition system in urban railway. The object recognition system that we propose is to analyze the camera images in the history and to recognize the situations where there are objects in the landing area and the waiting area that are not moving for more than a certain time. The proposed algorithm proved its effectiveness by showing detection rate of 100% for Selected area detection, 82% for detection in neglected object, and 94% for motionless object detection, compared with 84.62% object recognition rate using existing Kalman filter.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.3
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• pp.11-19
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• 2008

Background subtraction, which separates moving objects in video sequences, is an essential technology for object recognition and tracking. However, background subtraction methods are often confused by shadow regions and this misclassification of shadow regions disturbs further processes to perceive the shapes or exact positions of moving objects. This paper proposes a method for shadow elimination which is based on shadow modeling by color information and Bayesian classification framework. Also, because of dynamic update of modeling parametres, the proposed method is able to correspond adaptively to illumination changes. Experimental results proved that the proposed method can eliminate shadow regions effectively even for circumstances with varying lighting condition.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.786-791
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• 2021

In this paper, in order to minimize limitations such as signal interference and positioning errors in existing indoor positioning systems, a smart LED-based positioning system for excellent line-of-sight radio environments and precise location tracking is proposed to improve accuracy. An IEEE 802.4 Zigbee module is mounted on the SMPS board of a smart LED; RSSI and LQI signals are received from a moving tag, and the system is configured to transmit the measured data to the positioning server through a gateway. For the experiment, the necessary hardware, such as the gateway and the smart LED module, were separately designed, and the experiment was conducted after configuring the system in an external field office. The positioning error was within 70cm as a result of performing complex calculations in the positioning server after transmitting a vector value of the moving object obtained from the direction sensor, together with a signal from the moving object received by the smart LED. The result is a significantly improved positioning error, compared to an existing short-range wireless communications-based system, and shows the level at which commercial products can be implemented.

• Journal of Navigation and Port Research
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• v.41 no.5
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• pp.269-276
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• 2017

"Tracking" here refers to the estimation of a moving object with some degree of accuracy where at least one measurement is given. The measurement, which is the sensor-obtained output, contains systemic errors and errors that are due to the surrounding environment. Tracking filters play the key role of the target-state estimation after the updating of the tracking system; therefore, the type of filter that is used for the conduction of the estimations is crucial in the determining of the reliability of the updated value, and this is especially true since the performances of different filters vary when they are subjected to different environmental and initial conditions. The purpose of this paper is the conduction of a comparison between the performances of the ${\alpha}-{\beta}-{\gamma}$ filter and the Kalman filter regarding an ARPA-system tracking module that is used on board high-dynamic warships. The comparison is based on the capability of each filter to reduce noise and maintain a stable response. The residual error is computed from the difference between the true and predicted positions and the true and estimated positions for the given sample. The results indicate that the tracking accuracy of the Kalman filter is higher compared with that of the optimal ${\alpha}-{\beta}-{\gamma}$ filter; however, the response of the optimal ${\alpha}-{\beta}-{\gamma}$ filter is more stable.

• Journal of the Korean Geographical Society
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• v.47 no.3
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• pp.426-439
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• 2012

The goal of this study is to propose a new method to capture the qualitative person spatial behavior. Beyond tracking or indexing the change of the location of a person, the changes in the relationships between a person and its environment are considered as the main source for the formal model of this study. Specifically, this paper focuses on the movement behavior of a person near the boundary of a region. To capture the behavior of person near the boundary of regions, a new formal approach for integrating an object's scope of influence is described. Such an object, a spatio-temporally extended point (STEP), is considered here by addressing its scope of influence as potential events or interactions area in conjunction with its location. The formalism presented is based on a topological data model and introduces a 12-intersection model to represent the topological relations between a region and the STEP in 2-dimensional space. From the perspective of STEP concept, a prototype analysis results are provided by using GPS tracking data in real world.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.552-560
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• 2021

Currently, the domestic aquaculture industry is pursuing smartization, but it is still proceeding with human subjective judgment in many processes in the aquaculture stage. The prerequisite for the smart aquaculture industry is to effectively grasp the condition of fish in the farm. If real-time monitoring is possible by identifying the number of fish populations, size, pathways, and speed of movement, various forms of automation such as automatic feed supply and disease determination can be carried out. In this study, we proposed an algorithm to identify the state of fish in real time using underwater video data. The fish detection performance was compared and evaluated by applying the latest deep learning-based object detection models, and an algorithm was proposed to measure fish object identification, path tracking, and moving speed in continuous image frames in the video using the fish detection results. The proposed algorithm showed 92% object detection performance (based on F1-score), and it was confirmed that it effectively tracks a large number of fish objects in real time on the actual test video. It is expected that the algorithm proposed in this paper can be effectively used in various smart farming technologies such as automatic feed feeding and fish disease prediction in the future.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.41-49
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• 2000

In this paper, we propose a new method of indexing and searching based on object-specific features at different semantic levels for video retrieval. A moving trajectory model is used as an indexing key for accessing the individual object in the semantic level. By tracking individual objects with segmented data, we can generate motion trajectories and set model parameters using polynomial curve fitting. The proposed searching scheme supports various types of queries including query by example, query by sketch, and query on weighting parameters for event-based video retrieval. When retrieving the interested video clip, the system returns the best matching event in the similarity order.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.101-110
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• 2004

The robots that will be needed in the near future are human-friendly robots that are able to coexist with humans and support humans effectively. To realize this, humans and robots need to be in close proximity to each other as much as possible. Moreover, it is necessary for their interactions to occur naturally. It is desirable for a robot to carry out human following, as one of the human-affinitive movements. The human-following robot requires several techniques: the recognition of the moving objects, the feature extraction and visual tracking, and the trajectory generation for following a human stably. In this research, a predictable intelligent space is used in order to achieve these goals. An intelligent space is a 3-D environment in which many sensors and intelligent devices are distributed. Mobile robots exist in this space as physical agents providing humans with services. A mobile robot is controlled to follow a walking human using distributed intelligent sensors as stably and precisely as possible. The moving objects is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the intelligent space. Uncertainties in the position estimation caused by the point-object assumption are compensated using the Kalman filter. To generate the shortest time trajectory to follow the walking human, the linear and angular velocities are estimated and utilized. The computer simulation and experimental results of estimating and following of the walking human with the mobile robot are presented.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.657-664
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• 2003

Reliable tracking of moving humans is essential to motion estimation, video surveillance and human-computer interface. This paper presents a new approach to human motion tracking that combines appearance-based and model-based techniques. Monocular color video is processed at both pixel level and object level. At the pixel level, a Gaussian mixture model is used to train and classily individual pixel colors. At the object level, a 3D human body model projected on a 2D image plane is used to fit the image data. Our method does not use inverse kinematics due to the singularity problem. While many others use stochastic sampling for model-based motion tracking, our method is purely dependent on nonlinear programming. We convert the human motion tracking problem into a nonlinear programming problem. A cost function for parameter optimization is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. Our method is used to recognize various human motions. Motion tracking results from video sequences are very encouraging.