• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.2
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• pp.41-50
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• 2023

Gait analysis is an important tool in the clinical management of cerebral palsy, allowing for the assessment of condition severity, identification of potential gait abnormalities, planning and evaluation of interventions, and providing a baseline for future comparisons. However, traditional methods of gait analysis are costly and time-consuming, leading to a need for a more convenient and continuous method. This paper proposes a method for analyzing the posture of cerebral palsy patients using only smartphone videos and deep learning models, including a ResNet-based image tilt correction, AlphaPose for human pose estimation, and SmoothNet for temporal smoothing. The indicators employed in medical practice, such as the imbalance angles of shoulder and pelvis and the joint angles of spine-thighs, knees and ankles, were precisely examined. The proposed system surpassed pose estimation alone, reducing the mean absolute error for imbalance angles in frontal videos from 4.196° to 2.971° and for joint angles in sagittal videos from 5.889° to 5.442°.

• Smart Media Journal
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• v.12 no.7
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• pp.59-67
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• 2023

Recently, research on behavior analysis tracking human posture and movement has been actively conducted. In particular, OpenPose, an open-source software developed by CMU in 2017, is a representative method for estimating human appearance and behavior. OpenPose can detect and estimate various body parts of a person, such as height, face, and hands in real-time, making it applicable to various fields such as smart healthcare, exercise training, security systems, and medical fields. In this paper, we propose a method for classifying four exercise movements - Squat, Walk, Wave, and Fall-down - which are most commonly performed by users in the gym, using OpenPose-based deep learning models, DNN and CNN. The training data is collected by capturing the user's movements through recorded videos and real-time camera captures. The collected dataset undergoes preprocessing using OpenPose. The preprocessed dataset is then used to train the proposed DNN and CNN models for exercise movement classification. The performance errors of the proposed models are evaluated using MSE, RMSE, and MAE. The performance evaluation results showed that the proposed DNN model outperformed the proposed CNN model.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.4
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• pp.512-519
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• 2012

This paper proposes a 3-D pose (positions and orientations) estimation system based on the recognition of circular ring patterns. To deal with monocular vision-based pose estimation problem, we specially design a circular ring pattern that has a simplicity merit in view of object recognition. A pose estimation procedure is described in detail, which utilizes the geometric transformation of a circular ring pattern in 2-D perspective projection space. The proposed method is evaluated through the analysis of accuracy and precision with respect to 3-D pose estimation of a quadrotor-type vehicle in 3-D space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.2
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• pp.800-816
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• 2018

In recent years, video surveillance research has been able to recognize various behaviors of pedestrians and analyze the overall situation of objects by combining image analysis technology and deep learning method. Human Activity Recognition (HAR), which is important issue in video surveillance research, is a field to detect abnormal behavior of pedestrians in CCTV environment. In order to recognize human behavior, it is necessary to detect the human in the image and to estimate the pose from the detected human. In this paper, we propose a novel approach for 2D Human Pose Estimation based on object detection using RGB-D information. By adding depth information to the RGB information that has some limitation in detecting object due to lack of topological information, we can improve the detecting accuracy. Subsequently, the rescaled region of the detected object is applied to ConVol.utional Pose Machines (CPM) which is a sequential prediction structure based on ConVol.utional Neural Network. We utilize CPM to generate belief maps to predict the positions of keypoint representing human body parts and to estimate human pose by detecting 14 key body points. From the experimental results, we can prove that the proposed method detects target objects robustly in occlusion. It is also possible to perform 2D human pose estimation by providing an accurately detected region as an input of the CPM. As for the future work, we will estimate the 3D human pose by mapping the 2D coordinate information on the body part onto the 3D space. Consequently, we can provide useful human behavior information in the research of HAR.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.31-42
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• 2019

With the recent development of deep learning technology, remarkable achievements have been made in many research areas of computer vision. Deep learning has also made dramatic improvement in two-dimensional or three-dimensional human pose estimation based on a single image, and many researchers have been expanding the scope of this problem. The human pose estimation is one of the most important research fields because there are various applications, especially it is a key factor in understanding the behavior, state, and intention of people in image or video analysis. Based on this background, this paper surveys research trends in estimating human poses based on a single image. Because there are various research results for robust and accurate human pose estimation, this paper introduces them in two separated subsections: 2D human pose estimation and 3D human pose estimation. Moreover, this paper summarizes famous data sets used in this field and introduces various studies which utilize human poses to solve their own problem.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.735-739
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• 2005

This paper presents a new system to estimate the head pose of human in interactive indoor environment that has dynamic illumination change and large working space. The main idea of this system is to suggest a new morphological feature for estimating head angle from stereo disparity map. When a disparity map is obtained from stereo camera, the matching confidence value can be derived by measurements of correlation of the stereo images. Applying a threshold to the confidence value, we also obtain the specific morphology of the disparity map. Therefore, we can obtain the morphological shape of disparity map. Through the analysis of this morphological property, the head pose can be estimated. It is simple and fast algorithm in comparison with other algorithm which apply facial template, 2D, 3D models and optical flow method. Our system can automatically segment and estimate head pose in a wide range of head motion without manual initialization like other optical flow system. As the result of experiments, we obtained the reliable head orientation data under the real-time performance.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.8
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• pp.50-56
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• 2004

The directional ability of an automobile has an influence on driver directly, and hence it must be given most priority. Alignment factors of automobile such as the camber, caster and toe directly affect the directional ability of a vehicle. The above mentioned factors are determined by the pose of interlinks in the assembly of an automobile front chassis module. Measuring the position of center point of ball joints in the front lower arm is very difficult. A method to determine this position is suggested in this paper. Pose estimation for front chassis module and dimensional evaluation to find the rotational characteristics of front lower arm were developed based on fundamental geometric techniques. To interpret the inspection data obtained for front chassis module, 3-D best fit method is needed. The best fit method determines the relationship between the nominal design coordinate system and the corresponding feature coordinate system. The least squares method based on singular value decomposition is used in this paper.

• The International Journal of Costume Culture
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• v.12 no.1
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• pp.13-25
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• 2009

This research is progressed to look out for efficient expression-elements of eroticism used in advertisements. Since these expressions of eroticism appealing to sex which is one of the primitive instincts of mankind are increasing in advertisements of cosmetic products which are used more often by recent high-rate-growth and the elevation of living conditions. The most usual expression-elements of eroticism in advertisement are exposure, pose, fashion style, make up, hair style and color. To analyze those expression-elements we made four pieces of fashion advertisement photos with four different types and surveyed both fashion majored students and non-fashion majored students. We applied regression analysis, ANOVA, and frequency analysis to verify the hypothesis. We found that in eroticism, the pose was the most important cognitive feature among the expression-elements and degree of cognition are varied according to major field and sexual interest. As a result, degree of cognition which effected by expression-elements will be varied even in same advertisement. In particular, convincing that the pose was the significant factor of eroticism cognition, expression of eroticism in advertisement would be more diverse and daring.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.69-75
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• 2023

In recent years, there has been a growing interest in deep learning-based animal pose estimation, especially in the areas of animal behavior analysis and healthcare. However, existing animal pose estimation techniques do not perform well when body parts are occluded or not present. In particular, the occlusion of dog tail or ear might lead to a significant degradation of performance in pet behavior and emotion recognition. In this paper, to solve this intractable problem, we propose a simple yet novel framework for pet pose estimation where pet pose is predicted on an outpainted image where some body parts hidden outside the input image are reconstructed by the image inpainting network preceding the pose estimation network, and we performed a preliminary study to test the feasibility of the proposed approach. We assessed CE-GAN and BAT-Fill for image outpainting, and evaluated SimpleBaseline for pet pose estimation. Our experimental results show that pet pose estimation on outpainted images generated using BAT-Fill outperforms the existing methods of pose estimation on outpainting-less input image.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2388-2398
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• 2017

In this study, we propose a robust face recognition system to pose variations based on automatic pose estimation. Radial basis function neural network is applied as one of the functional components of the overall face recognition system. The proposed system consists of preprocessing and recognition modules to provide a solution to pose variation and high-dimensional pattern recognition problems. In the preprocessing part, principal component analysis (PCA) and 2-dimensional 2-directional PCA ($(2D)^2$ PCA) are applied. These functional modules are useful in reducing dimensionality of the feature space. The proposed RBFNNs architecture consists of three functional modules such as condition, conclusion and inference phase realized in terms of fuzzy "if-then" rules. In the condition phase of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by the Fuzzy C-Means (FCM) algorithm. In conclusion phase of rules, the connections (weights) are realized through four types of polynomials such as constant, linear, quadratic and modified quadratic. The coefficients of the RBFNNs model are obtained by fuzzy inference method constituting the inference phase of fuzzy rules. The essential design parameters (such as the number of nodes, and fuzzification coefficient) of the networks are optimized with the aid of Particle Swarm Optimization (PSO). Experimental results completed on standard face database -Honda/UCSD, Cambridge Head pose, and IC&CI databases demonstrate the effectiveness and efficiency of face recognition system compared with other studies.