• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권5호
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• pp.1856-1869
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• 2015

This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권4호
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• pp.332-339
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• 2014

Face classification has wide applications in security and surveillance. However, this technique presents various challenges caused by pose, illumination, and expression changes. Face recognition with long-distance images involves additional challenges, owing to focusing problems and motion blurring. Multiple frames under varying spatial or temporal settings can acquire additional information, which can be used to achieve improved classification performance. This study investigates the effectiveness of multi-frame decision-level fusion with photon-counting linear discriminant analysis. Multiple frames generate multiple scores for each class. The fusion process comprises three stages: score normalization, score validation, and score combination. Candidate scores are selected during the score validation process, after the scores are normalized. The score validation process removes bad scores that can degrade the final output. The selected candidate scores are combined using one of the following fusion rules: maximum, averaging, and majority voting. Degraded facial images are employed to demonstrate the robustness of multi-frame decision-level fusion in harsh environments. Out-of-focus and motion blurring point-spread functions are applied to the test images, to simulate long-distance acquisition. Experimental results with three facial data sets indicate the efficiency of the proposed decision-level fusion scheme.

• 대한물리의학회지
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• 제3권3호
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• pp.169-176
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• 2008

Purpose : Tandem stance is a clinical measure of standing balance considered to assess postural steadiness in a heel-to-toe position by a temporal measurement. The aim of this study is to investigate postural steadiness and to explore the weight distribution between legs during 25s of quiet stance and tandem stance(right foot was leading) in healthy young adults. Methods : 107 healthy young adults(mean age 21.1 years) are participated. Weight distribution beneath both feet and sway distance were recorded while the subjects performed 25s of quiet stance and tandem stance. Results : Subjects placed more weight on the rear leg in tandem stance and on the left foot in quiet stance. So, quiet stance and tandem stance is not a task for equal weight bearing. Subjects show larger sway distance in anteroposterior direction of tandem stance than quiet stance. Conclusion : The results of this study will be useful to researchers and clinicians using tandem stance measures to evaluate postural steadiness and to predict fall. The results suggest that tandem stance is useful to treat of weight distribution and to improve of balace in elderly adults and stroke patients.

• Journal of Information Processing Systems
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• 제19권2호
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• pp.258-266
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• 2023

Aiming at the problems of poor customer satisfaction and poor accuracy of customer classification, this paper proposes a customer classification model based on speech recognition. First, this paper analyzes the temporal data characteristics of customer demand data, identifies the influencing factors of customer demand behavior, and determines the process of feature extraction of customer voice signals. Then, the emotional association rules of customer demands are designed, and the classification model of customer demands is constructed through cluster analysis. Next, the Euclidean distance method is used to preprocess customer behavior data. The fuzzy clustering characteristics of customer demands are obtained by the fuzzy clustering method. Finally, on the basis of naive Bayesian algorithm, a customer demand classification model based on speech recognition is completed. Experimental results show that the proposed method improves the accuracy of the customer demand classification to more than 80%, and improves customer satisfaction to more than 90%. It solves the problems of poor customer satisfaction and low customer classification accuracy of the existing classification methods, which have practical application value.

• 서비스연구
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• 제7권4호
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• pp.1-14
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• 2017

과시소비에 관한 연구는 많이 진행되어 왔지만, 소비자가 지각한 타인의 과시소비의 부정적인 감정에 대한 연구는 많지 않다. 본 연구에서는 타인의 과시 소비가 소비자의 브랜드 평가에 미치는 부정적인 영향을 검증하는 것을 목적으로 실험을 진행하였다. 본 연구에서는 두개의 실험을 통하여, 지각한 과시 소비는 소비자의 브랜드 평가에 부정적인 영향을 끼친다는 것을 밝혀냈다. 타인의 과시 소비는 임시적으로 소비자의 권력거리신념에 영향을 미치며 이러한 영향은 궁극적으로 소비자의 브랜드평가에 영향을 미친다. 즉 소비자의 권력거리신념은 타인의 과시소비와 브랜드평가의 관계를 매개한다. 개인 수준의 집단 규범은 타인의 과시 소비와 브랜드 평가에 대한 조절효과를 밝히지 못했지만, 국가차원의 집단규범은 타인의 과시소비와 브랜드평가의 관계를 조절하는 것을 검증하였다.

• 한국항해항만학회지
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• 제27권3호
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• pp.273-282
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• 2003

This paper proposes a sensor-fusion technique where rho data sets for the previous moments are properly transformed and fused into the current data sets to enable accurate measurement, such as, distance to an obstacle and location of the service robot itself. In the conventional fusion schemes, the measurement is dependent only on the current data sets. As the results, more of sensors are required to measure a certain physical promoter or to improve the accuracy of the measurement. However, in this approach, intend of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the measurement improvement. Theoretical basis is illustrated by examples md the effectiveness is proved through the simulation. Finally, the new space and time sensor fusion (STSF) scheme is applied to the control of a mobile robot in the indoor environment and the performance was demonstrated by the real experiments.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.927-939
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• 2020

Gait recognition can identify people's identity from a long distance, which is very important for improving the intelligence of the monitoring system. Among many human features, gait features have the advantages of being remotely available, robust, and secure. Traditional gait feature extraction, affected by the development of behavior recognition, can only rely on manual feature extraction, which cannot meet the needs of fine gait recognition. The emergence of deep convolutional neural networks has made researchers get rid of complex feature design engineering, and can automatically learn available features through data, which has been widely used. In this paper,conduct feature metric learning in the three-dimensional space by combining the three-dimensional convolution features of the gait sequence and the Siamese structure. This method can capture the information of spatial dimension and time dimension from the continuous periodic gait sequence, and further improve the accuracy and practicability of gait recognition.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.471-475
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• 2003

This paper proposes a sensor-fusion technique where the data sets for the previous moments are properly transformed and fused into the current data sets to enable accurate measurement, such as, distance to an obstacle and location of the service robot itself. In the conventional fusion schemes, the measurement is dependent on the current data sets. As the results, more of sensors are required to measure a certain physical parameter or to improve the accuracy of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the measurement improvement. Theoretical basis is illustrated by examples and the effectiveness is proved through the simulations. Finally, the new space and time sensor fusion (STSF) scheme is applied to the control of a mobile robot in an unstructured environment as well as structured environment.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권1호
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• pp.23-28
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• 2004

This paper proposes a sensor-fusion technique where the data sets for the previous moments are properly transformed and fused into the current data sets to enable accurate measurement, such as, distance to an obstacle and location of the service robot itself. In the conventional fusion schemes, the measurement is dependent on the current data sets. As the results, more of sensors are required to measure a certain physical parameter or to improve the accuracy of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the measurement improvement. Theoretical basis is illustrated by examples and the effectiveness is proved through the simulations. Finally, the new space and time sensor fusion(STSF) scheme is applied to the control of a mobile robot in an unstructured environment as well as structured environment.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권1호
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• pp.27-34
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• 2015

For a vision-based driver assistance system, unusual motion detection is one of the important means of preventing accidents. In this paper, we propose a real-time unusual-motion-detection model, which contains two stages: salient region detection and unusual motion detection. In the salient-region-detection stage, we present an improved temporal attention model. In the unusual-motion-detection stage, three kinds of factors, the speed, the motion direction, and the distance, are extracted for detecting unusual motion. A series of experimental results demonstrates the proposed method and shows the feasibility of the proposed model.