• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.59-68
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• 2005

This paper proposes a face recognition under varying poses using local area obtained by side-view pose normalization. General normalization methods for face recognition under varying pose have a problem with the information about invisible area of face. Generally this problem is solved by compensation, but there are many cases where the image is distorted or features lost due to compensation .To solve this problem we normalize the face pose in side-view to reduce distortion that happens mainly in areas that have large depth variation. We only use undistorted area, removing the area that has been distorted by normalization. We consider two cases of yaw pose variation and pitch pose variation, and by experiments, we confirm the improvement of recognition performance.

• 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.

• The Journal of Korea Robotics Society
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• v.12 no.3
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• pp.313-321
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• 2017

One of the most frequently performed tasks in human-robot interaction (HRI), intelligent vehicles, and security systems is face related applications such as face recognition, facial expression recognition, driver state monitoring, and gaze estimation. In these applications, accurate head pose estimation is an important issue. However, conventional methods have been lacking in accuracy, robustness or processing speed in practical use. In this paper, we propose a novel method for estimating head pose with a monocular camera. The proposed algorithm is based on a deep neural network for multi-task learning using a small grayscale image. This network jointly detects multi-view faces and estimates head pose in hard environmental conditions such as illumination change and large pose change. The proposed framework quantitatively and qualitatively outperforms the state-of-the-art method with an average head pose mean error of less than $4.5^{\circ}$ in real-time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1347-1355
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• 2015

In this study, we compare the recognition performance using PCA and RBFNNs for introducing robust face recognition system to pose variations based on pose estimation. proposed face recognition system uses Honda/UCSD database for comparing recognition performance. Honda/UCSD database consists of 20 people, with 5 poses per person for a total of 500 face images. Extracted image consists of 5 poses using Multiple-Space PCA and each pose is performed by using (2D)²PCA for performing pose classification. Linear polynomial function is used as connection weight of RBFNNs Pattern Classifier and parameter coefficient is set by using Particle Swarm Optimization for model optimization. Proposed (2D)²PCA-based face pose classification performs recognition performance with PCA, (2D)²PCA and RBFNNs.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.677-682
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• 2010

This paper addresses a method of estimating roughly the human pose by comparing Haar-wavelet value which is learned in face detection technology using AdaBoost algorithm. We also presents its application to face recognition. The learned weak classifier is used to a Haar-wavelet robust to each pose's feature by comparing the coefficients during the process of face detection. The Mahalanobis distance is used to measure the matching degree in Haar-wavelet selection. When a facial image is detected using the selected Haar-wavelet, the pose is estimated. The proposed pose estimation can be used to improve face recognition speed. Experiments are conducted to evaluate the performance of the proposed method for pose estimation.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.12
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• pp.1021-1026
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• 2016

The graph-based SLAM (Simultaneous Localization and Mapping) approach has been gaining much attention in SLAM research recently thanks to its ability to provide better maps and full trajectory estimations when compared to the filtering-based SLAM approach. Even though graph-based SLAM requires batch processing causing it to be computationally heavy, recent advancements in optimization and computing power enable it to run fast enough to be used in real-time. However, data association problems still require large amount of computation when building a pose graph. For example, to find loop closures it is necessary to consider the whole history of the robot trajectory and sensor data within the confident range. As a pose graph grows, the number of candidates to be searched also grows. It makes searching the loop closures a bottleneck when solving the SLAM problem. Our approach to alleviate this bottleneck is to sample a limited number of pose nodes in which loop closures are searched. We propose a heuristic for sampling pose nodes that are most advantageous to closing loops by providing a way of ranking pose nodes in order of usefulness for closing loops.

• The KIPS Transactions:PartB
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• v.18B no.1
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• pp.11-20
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• 2011

Pictorial Structures is known as an effective method that recognizes and tracks human poses. In this paper, the upper body pose is also tracked by PS and a particle filter(PF). PF is one of dynamic programming methods. But Markov chain-based dynamic motion model which is used in dynamic programming methods such as PF, couldn't predict effectively the highly articulated upper body motions. Therefore PF often fails to track upper body pose. In this paper we propose the key pose-based proposal distribution for proper particle prediction based on the similarities between key poses and an upper body silhouette. In the experimental results we confirmed our 70.51% improved performance comparing with a conventional method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2328-2344
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• 2022

2D human pose estimation still faces difficulty in low-resolution images. Most existing top-down approaches scale up the target human bonding box images to the large size and insert the scaled image into the network. Due to up-sampling, artifacts occur in the low-resolution target images, and the degraded images adversely affect the accurate estimation of the joint positions. To address this issue, we propose a multi-resolution input feature fusion network for human pose estimation. Specifically, the bounding box image of the target human is rescaled to multiple input images of various sizes, and the features extracted from the multiple images are fused in the network. Moreover, we introduce a guiding channel which induces the multi-resolution input features to alternatively affect the network according to the resolution of the target image. We conduct experiments on MS COCO dataset which is a representative dataset for 2D human pose estimation, where our method achieves superior performance compared to the strong baseline HRNet and the previous state-of-the-art methods.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.427-435
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• 2023

This paper proposes a method of estimating the pose of a mobile robot by using a learning model. When estimating the pose of a mobile robot, wheel encoder and inertial measurement unit (IMU) data are generally utilized. However, depending on the condition of the ground surface, slip occurs due to interaction between the wheel and the floor. In this case, it is hard to predict pose accurately by using only encoder and IMU. Thus, in order to reduce pose error even in such conditions, this paper introduces a pose estimation method based on a learning model using data of the wheel encoder and IMU. As the learning model, long short-term memory (LSTM) network is adopted. The inputs to LSTM are velocity and acceleration data from the wheel encoder and IMU. Outputs from network are corrected linear and angular velocity. Estimated pose is calculated through numerically integrating output velocities. Dataset used as ground truth of learning model is collected in various ground conditions. Experimental results demonstrate that proposed learning model has higher accuracy of pose estimation than extended Kalman filter (EKF) and other learning models using the same data under various ground conditions.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.517-522
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• 1991

In this paper, robot's grasping poses are classified into three cases, and force/torque information in each grasping pose is analyzed. In the grasping process, error between the desired and the actual grasping poses can be generated because of uncertainty in the environment. A systematic algorithm is presented, that uses the force/torque information generated by grasping pose error to estimate robot's actual grasping pose.