• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.91-98
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• 2023

Human-object interaction(HOI) detection requires both object detection and interaction recognition, and requires a large amount of data to learn a detection model. Current opened dataset is insufficient in scale for training model enough. In this paper, we propose an easy and effective data augmentation method called Simple Quattro Augmentation(SQA) and Random Quattro Augmentation(RQA) for human-object interaction detection. We show that our proposed method can be easily integrated into State-of-the-Art HOI detection models with HICO-DET dataset.

• ETRI Journal
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• v.42 no.2
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• pp.230-238
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• 2020

Human-object interaction (HOI) detection is a popular computer vision task that detects interactions between humans and objects. This task can be useful in many applications that require a deeper understanding of semantic scenes. Current HOI detection networks typically consist of a feature extractor followed by detection layers comprising small filters (eg, 1 × 1 or 3 × 3). Although small filters can capture local spatial features with a few parameters, they fail to capture larger context information relevant for recognizing interactions between humans and distant objects owing to their small receptive regions. Hence, we herein propose a three-stream HOI detection network that employs a context convolution module (CCM) in each stream branch. The CCM can capture larger contexts from input feature maps by adopting combinations of large separable convolution layers and residual-based convolution layers without increasing the number of parameters by using fewer large separable filters. We evaluate our HOI detection method using two benchmark datasets, V-COCO and HICO-DET, and demonstrate its state-of-the-art performance.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.05a
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• pp.219-222
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• 2009

Rapid object recognition is one of the main stream research themes focusing to reveal how human recognizes object and interacts with environment in natural world. This field of study is of consequence in that it is highly important in evolutionary perspective to quickly see the external objects and judge their characteristics to plan future reactions. In this study, we investigated how human detect natural scene objects and categorize them in a limited time frame. We applied Magnetoencepahlogram (MEG) while participants were performing detection (e.g. object vs. texture) or basic-level categorization (e.g. cars vs. dogs) tasks to track the dynamic interaction in human brain for rapid object recognition process. The results revealed that detection and categorization involves different temporal and functional connections that correlated for the successful recognition process as a whole. These results imply that dynamics in the brain are important for our interaction with environment. The implication from this study can be further extended to investigate the effect of subconscious emotional factors on the dynamics of brain interactions during the rapid recognition process.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.570-572
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• 2006

This paper discusses the method of moving object detection for biped robot walking. Most researches on vision based object detection have mostly focused on fixed camera based algorithm itself. However, developing vision systems for biped walking robot is an important and urgent issue since hired walking robots are ultimately developed not only for researches but to be utilized in real life. In the research, method for moving object detection has been developed for task assignment and execution of biped robot as well as for human robot interaction (HRI) system. But these methods are not suitable to biped walking robot. So, we suggest the advanced method which is suitable to biped walking robot platform. For carrying out certain tasks, an object detecting system using modified optical flow algorithm by wireless vision camera is implemented in a biped walking robot.

• The Journal of Korea Robotics Society
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• v.18 no.4
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• pp.463-471
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• 2023

This paper proposes a method to estimate the level of interest shown by visitors towards a specific target, a guide robot, in spaces where a large number of visitors, such as exhibition halls and museums, can show interest in a specific subject. To accomplish this, we apply deep learning-based behavior recognition and object tracking techniques for multiple visitors, and based on this, we derive the behavior analysis and interest level of visitors. To implement this research, a personalized dataset tailored to the characteristics of exhibition hall and museum environments was created, and a deep learning model was constructed based on this. Four scenarios that visitors can exhibit were classified, and through this, prediction and experimental values were obtained, thus completing the validation for the interest estimation method proposed in this paper.

• Korean Journal of Computational Design and Engineering
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• v.18 no.5
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• pp.374-383
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• 2013

Various interaction techniques have been studied for providing the feeling of touch and improve immersion in augmented reality (AR) environments. Tangible AR interaction exploiting two types (product-type and pointer-type) of simple objects has earned great interest for cost-effective design evaluation of digital handheld products. When the sizes of markers attached to the objects are kept big to obtain better marker recognition, the pointer-type object frequently and significantly occludes the product-type object, which deteriorates natural visualization and level of immersion in an AR environment. In this paper, in order to overcome such problems, we propose tangible AR interaction using fingertip touch combined with small-sized markers. The proposed approach facilitates the use of convex polygons to recover the boundaries of AR markers which are partially occluded. It also properly enlarges the pattern area of each AR marker to reduce the sizes of AR markers without sacrificing the quality of marker detection. We empirically verified the quality of the proposed approach, and applied it in the process of design evaluation of digital products. From experimental results, we found that the approach is comparably accurate enough to be applied to the design evaluation process and tangible enough to provide a pseudo feeling of manipulating virtual products with human hands.

• The Journal of the Korea Contents Association
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• v.11 no.4
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• pp.215-224
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• 2011

There have been studying many researches to detect human body and to track one with increasing interest on human and computer interaction. In this paper, we propose the algorithm that automatically extracts joints, linked points of human body, using the ratio of human body under single camera and tracks object. The proposed method gets the difference images of the grayscale images and ones of the hue images between input image and background image. Then the proposed method composes the results, splits background and foreground, and extracts objects. Also we standardize the ratio of human body using face' length and the measurement of human body and automatically extract joints of the object using the ratio and the corner points of the silhouette of object. After then, we tract the joints' movement using block-matching algorithm. The proposed method is applied to test video to be acquired through a camera and the result shows that the proposed method automatically extracts joints and effectively tracks the detected joints.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.4
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• pp.851-859
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• 2011

In this paper, we propose the method that automatically extracts the silhouette and the joints of consecutive input image, and track joints to trace object for interaction between human and computer. Also the proposed method presents the action of human being to map human body using joints. To implement the algorithm, we model human body using 14 joints to refer to body size. The proposed method converts RGB color image acquired through a single camera to hue, saturation, value images and extracts body's silhouette using the difference between the background and input. Then we automatically extracts joints using the corner points of the extracted silhouette and the data of body's model. The motion of object is tracted by applying block-matching method to areas around joints among all image and the human's motion is mapped using positions of joints. The proposed method is applied to the test videos and the result shows that the proposed method automatically extracts joints and effectively maps human body by the detected joints. Also the human's action is aptly expressed to reflect locations of the joints

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.621-624
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• 2011

Recently, augmented reality is divided into broadly marker based and markerless based as part of HCI (Human Computer Interaction). Markerless based is augmented object using natural features in real-world environment. On the other hand, Marker based is use to calculate easily the coordinates and exactly augmented object using flat marker of rectangular. However, marker-based image is provided due to the presence of the marker in a markerless, giving users a more less realistic and immersive view. In this paper, We research about combined diminished reality and augmented reality for Marker-Based Augmented Reality System Structure using Object Removal Tchnique in order to increase realistic and immersive view.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.1110-1111
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• 2019

비전 기반 제스처 인식은 비 침입적이고 저렴한 비용으로 자연스러운 인간-컴퓨터 상호 작용을 제공한다. 로봇의 사용이 증가함에 따라 인간-로봇 상호 작용은 점점 더 중요해질 것이다. 최근 효율적인 딥러닝 기술이 연구되고 있다. 본 연구는 인간 컴퓨터 상호 작용을 위해 CNN을 기반으로 한 얼굴 및 손 동작의 인식을 위해 객체 검출 기법의 적용 결과를 제시한다.