• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.250-257
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• 2011

In this paper, we proposed the method detecting multiple pedestrians using motion information and SVM(Support Vector Machine) from a moving camera image. First, we detect moving pedestrians from both the difference image and the projection histogram which is compensated for the camera ego-motion using corresponding feature sets. The difference image is simple method but it is not detected motionless pedestrians. Thus, to fix up this problem, we detect motionless pedestrians using SVM The SVM works well particularly in binary classification problem such as pedestrian detection. However, it is not detected in case that the pedestrians are adjacent or they move arms and legs excessively in the image. Therefore, in this paper, we proposed the method detecting motionless and adjacent pedestrians as well as people who take excessive action in the image using motion information and SVM The experimental results on our various test video sequences demonstrated the high efficiency of our approach as it had shown an average detection ratio of 94% and False Positive of 2.8%.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.2
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• pp.124-132
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• 2013

This study investigated the feasibility of gait recognition using points on the body in three-dimensional (3D) space based on comparisons of four different feature vectors. To obtain the point trajectories on the body in 3D, gait motion data were captured from 10 participants using a 3D motion capture system, and four shoes with different heel heights were used to study the effects of heel height on gait recognition. Finally, the recognition rates were compared using four methods and different heel heights.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2327-2347
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• 2018

Human actions can be recognized from depth sequences. In the proposed algorithm, we initially construct depth, motion maps (DMM) by projecting each depth frame onto three orthogonal Cartesian planes and add the motion energy for each view. The body part of the action (BPoA) is calculated by using bounding box with an optimal window size based on maximum spatial and temporal changes for each DMM. Furthermore, feature vector is constructed by using BPoA for each human action view. In this paper, we employed an ensemble based learning approach called Rotation Forest to recognize different actions Experimental results show that proposed method has significantly outperforms the state-of-the-art methods on Microsoft Research (MSR) Action 3D and MSR DailyActivity3D dataset.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.1-11
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• 2006

In this paper, we propose the novel video retrieval algorithm using non-parametric based motion classification in the shot-based video indexing structure. The proposed system firstly gets the key frame and motion information from each shot segmented by scene change detection method, and then extracts visual features and non-parametric based motion information from them. Finally, we construct real-time retrieval system supporting similarity comparison of these spatio-temporal features. After the normalized motion vector fields is created from MPEG compressed stream, the extraction of non-parametric based motion feature is effectively achieved by discretizing each normalized motion vectors into various angle bins, and considering a mean, a variance, and a direction of these bins. We use the edge-based spatial descriptor to extract the visual feature in key frames. Experimental evidence shows that our algorithm outperforms other video retrieval methods for image indexing and retrieval. To index the feature vectors, we use R*-tree structures.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.1C
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• pp.78-85
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• 2007

With the advancement of the digital broadcasting and popularity of the Internet, recently, many studies are making on the digital watermarking for the copyright protection of digital data. This paper proposes the minimum modification method of motion vector to minimize the degradation of video quality, hiding subtitles of many language and information of OST(original sound track), character profiles, etc. as well as the copyright protection. Our proposed algorithm extracts feature vector by comparing motion vector data with watermark data, and minimize the modification of motion vectors by deciding the inversion of bit. Thus the degradation of video quality is minimized comparing to conventional algorithms. This algorithm also can check data integrity, and retrieve embedded hidden data simply and blindly. And our proposed scheme can be useful for conventional MPEG-1, -2 standards without any increment of bit rate in the compressed video domain. The experimental result shows that the proposed scheme obtains better video quality than other previous algorithms by about $0.5{\sim}1.5dB$.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.502-504
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• 1995

In video image compression, it is important to extract the exact notion information from image sequence in order to perform the data compression, the field rate conversion, and the motion compensated interpolation effectively. It is well known that the location of the smallest sum of absolute difference(SAD) does not always give the true motion vector(MV) since the MV obtained via full block search is often corrupted by noise. In this paper, we first classifies the input blocks into 3 categories : the background, the shade-motion, and the edge-motion. According to the characteristics of the classified blocks, multiple locations of relatively small SAD are searched with an adaptive search window by using the proposed method. The proposed method picks MVs among those candidates by using temporal correlation. Since temporal correlation reveals the noise level in a particular region of the video image sequence, we are able to reduce the search are very effectively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.5
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• pp.54-63
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• 2000

In this paper, we implemented cyber character that can do speech recognition, speech synthesis, Motion tracking and 3D animation. For speech recognition, we used Discrete-HMM algorithm with K-means 128 level vector quantization and MFCC feature vector. For speech synthesis, we used demi-syllables TD-PSOLA algorithm. For PC based Motion tracking, we present Fast Optical Flow like Method. And for animating 3D model, we used vertex interpolation with DirectSD retained mode. Finally, we implemented cyber character integrated above systems, which game calculating by the multiplication table with user and the cyber character always look at user using of Motion tracking system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2824-2838
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• 2013

There has been a lot of attention paid recently to analyze dynamic human gestures that vary over time. Most attention to dynamic gestures concerns with spatio-temporal features, as compared to analyzing each frame of gestures separately. For accurate dynamic gesture recognition, motion feature extraction algorithms need to find representative features that uniquely identify time-varying gestures. This paper proposes a new feature-extraction algorithm using temporal self-similarity based on a hierarchical human model. Because a conventional temporal self-similarity method computes a whole movement among the continuous frames, the conventional temporal self-similarity method cannot recognize different gestures with the same amount of movement. The proposed model-based temporal self-similarity method groups body parts of a hierarchical model into several sets and calculates movements for each set. While recognition results can depend on how the sets are made, the best way to find optimal sets is to separate frequently used body parts from less-used body parts. Then, we apply a multiclass support vector machine whose optimization algorithm is based on structural support vector machines. In this paper, the effectiveness of the proposed feature extraction algorithm is demonstrated in an application for taebo gesture recognition. We show that the model-based temporal self-similarity method can overcome the shortcomings of the conventional temporal self-similarity method and the recognition results of the model-based method are superior to that of the conventional method.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.143-146
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• 2000

Motion Estimation/compensation(ME/MC) is one of the efficient interframe ceding techniques for its ability to reduce the high redundancy between successive frames of an image sequence. Calculating the blocking matching takes most of the encoding time. In this paper a new fast block matching algorithm(BMA) is developed for motion estimation and for reduction of the computation time to search motion vectors. The feature of the new algorithm comes from the center-biased checking concept and the trend of pixel movements. At first, Motion Vector(MV) is searched in ${\pm}$1 of search area and then the motion estimation is exploited in the rest block. The ASP and MSE of the proposed search algorithm show good performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.1
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• pp.29-34
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• 2015

Brain-computer interface (BCI) is being studied for convenient life in various application fields. The purpose of this study is to investigate a changing electroencephalography (EEG) for precise motion of a robot or an artificial arm. Three subjects who participated in this experiment performed three-task: Grip, Move, Relax. Acquired EEG data was extracted feature data using two feature extraction algorithm (power spectrum analysis and multi-common spatial pattern). Support vector machine (SVM) were applied the extracted feature data for classification. The classification accuracy was the highest at Grip class of two subjects. The results of this research are expected to be useful for patients required prosthetic limb using EEG.