• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3C
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• pp.274-278
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• 2009

Recently, the research which concerns the object class recognition has been done. Although an object tracking based on most of histograms employs a colored model to improve robustness, the system is not reliable enough yet. In this paper, we presents a method to express and track an object by using the histograms which are composed with visual features through successive frames. The experimental results shows that this method is reliable to track a car within 80m distance from camera.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.449-460
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• 2005

This paper proposes a method to track persons robustly in illumination changes and partial occlusions in color video frames acquired from a fixed camera. To solve a problem of changing appearance by illumination change, a time-independent intrinsic image is used to remove noises in an frame and is adaptively updated frame-by-frame. We use a hierarchical human model including body color information in order to track persons in occlusion. The tracked human model is recorded into a persons' list for some duration after the corresponding person's exit and is recovered from the list after her reentering. The proposed method was experimented in several indoor and outdoor scenario. This demonstrated the potential effectiveness of an adaptive model-base method that corrected distorted person's color information by lighting changes, and succeeded tracking of persons which was overlapped in a frame.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5A
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• pp.375-387
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• 2012

This paper proposes a new real-time object tracking algorithm using particle filters with color and texture features in moving CCD camera images. If the user selects an initial object, this region is declared as a target particle and an initial state is modeled. Then, N particles are generated based on random distribution and CS-LBP (Centre Symmetric Local Binary Patterns) for texture model and weighted color distribution is modeled from each particle. For observation likelihoods estimation, Bhattacharyya distance between particles and their feature models are calculated and this observation likelihoods are used for weights of individual particles. After weights estimation, a new particle which has the maximum weight is selected and new particles are re-sampled using the maximum particle. For performance comparison, we tested a few combinations of features and particle filters. The proposed algorithm showed best object tracking performance when we used color and texture model simultaneously for likelihood estimation.

• ETRI Journal
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• v.39 no.2
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• pp.151-162
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• 2017

With the remarkable growth in rich media in recent years, people are increasingly exposed to visual information from the environment. Visual information continues to play a vital role in rich media because people's real interests lie in dynamic information. This paper proposes a novel discrete dynamic swarm optimization (DDSO) algorithm for video object tracking using invariant features. The proposed approach is designed to track objects more robustly than other traditional algorithms in terms of illumination changes, background noise, and occlusions. DDSO is integrated with a matching procedure to eliminate inappropriate feature points geographically. The proposed novel fitness function can aid in excluding the influence of some noisy mismatched feature points. The test results showed that our approach can overcome changes in illumination, background noise, and occlusions more effectively than other traditional methods, including color-tracking and invariant feature-tracking methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.586-591
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• 2009

In this paper, we propose the mobile object tracking algorithm based on the feature vector using particle filter. To do this, first, we detect the movement area of mobile object by using RGB color model and extract the feature vectors of the input image by using the KLT-algorithm. And then, we get the first feature vectors by matching extracted feature vectors to the detected movement area. Second, we detect new movement area of the mobile objects by using RGB and HSI color model, and get the new feature vectors by applying the new feature vectors to the snake algorithm. And then, we find the second feature vectors by applying the second feature vectors to new movement area. So, we design the mobile object tracking algorithm by applying the second feature vectors to particle filter. Finally, we validate the applicability of the proposed method through the experience in a complex environment.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.1-13
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• 2002

In this paper, we propose an object tracking system which can be convinced of moving area shaped on objects through color sequential images, decided moving directions of foot messengers or vehicles of image sequences. In static camera, we suggests a new evaluating method extracting co-occurrence matrix with feature vectors of RGB after analyzing and blocking difference images, which is accessed to field of camera view for motion. They are energy, entropy, contrast, maximum probability, inverse difference moment, and correlation of RGB color vectors. we describe how to analyze and compute corresponding relations of objects between adjacent frames. In the clustering, we apply an algorithm of FCM(fuzzy c means) to analyze matching and clustering problems of adjacent frames of the featured vectors, energy and entropy, gotten from previous phase. In the matching phase, we also propose a method to know correspondence relation that can track motion each objects by clustering with similar area, compute object centers and cluster around them in case of same objects based on membership function of motion area of adjacent frames.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.89-96
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• 2010

In this paper, we proposed a method about improving face tracking efficiency of face detection for AF system using the faces to the ROI. The conventional face detection system detecting faces based skin color uses the ratio of skin pixels of the present frame to detected face regions of the past frame to track the faces. The tracking method is superior in the stability of the regions but it is inferior in the face tracking efficiency. We proposed a face tracking method using the area of the overlapping region in the detected face regions of the past frame and the present frame to improve the tracking efficiency. The proposed face tracking efficiency demonstration was performed by making a film of face detection with face tracking in real-time and using the moving traces of the detected faces.

• The Journal of Korea Robotics Society
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• v.16 no.4
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• pp.336-344
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• 2021

This paper presents a robust deep learning-based human tracking framework in crowded environments. For practical human tracking applications, a target must be robustly tracked even in undetected or overcrowded situations. The proposed framework consists of two parts: robust deep learning-based human detection and tracking while recognizing the aforementioned situations. In the former part, target candidates are detected using Detectron2, which is one of the powerful deep learning tools, and their weights are computed and assigned. Subsequently, a candidate with the highest weight is extracted and is utilized to track the target human using a Kalman filter. If the bounding boxes of the extracted candidate and another candidate are overlapped, it is regarded as a crowded situation. In this situation, the center information of the extracted candidate is compensated using the state estimated prior to the crowded situation. When candidates are not detected from Detectron2, it means that the target is completely occluded and the next state of the target is estimated using the Kalman prediction step only. In two experiments, people wearing the same color clothes and having a similar height roam around the given place by overlapping one another. The average error of the proposed framework was measured and compared with one of the conventional approaches. In the error result, the proposed framework showed its robustness in the crowded environments.

• Journal of Internet Computing and Services
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• v.4 no.4
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• pp.9-14
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• 2003

A GUI (graphical user interface) has been a dominant platform for HCI (human computer interaction). A GUI - based interaction has made computers simpler and easier to use. The GUI - based interaction, however, does not easily support the range of interaction necessary to meet users' needs that are natural. intuitive, and adaptive. In this paper, the modified BMA (block matching algorithm) is proposed to track a hand in a sequence of an image and to recognize it in each video frame in order to replace a mouse with a pointing device for a virtual reality. The HCI system with 30 frames per second is realized in this paper. The modified BMA is proposed to estimate a position of the hand and segmentation with an orientation of motion and a color distribution of the hand region for real - time processing. The experimental result shows that the modified BMA with the YCbCr (luminance Y, component blue, component red) color coordinate guarantees the real - time processing and the recognition rate. The hand tracking by the modified BMA can be applied to a virtual reclity or a game or an HCI system for the disable.

• 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.