• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1857-1862
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• 2016

Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.337-351
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• 2021

Automatically recognizing facial expressions in video sequences is a challenging task because there is little direct correlation between facial features and subjective emotions in video. To overcome the problem, a video facial expression recognition method using spatiotemporal recurrent neural network and feature fusion is proposed. Firstly, the video is preprocessed. Then, the double-layer cascade structure is used to detect a face in a video image. In addition, two deep convolutional neural networks are used to extract the time-domain and airspace facial features in the video. The spatial convolutional neural network is used to extract the spatial information features from each frame of the static expression images in the video. The temporal convolutional neural network is used to extract the dynamic information features from the optical flow information from multiple frames of expression images in the video. A multiplication fusion is performed with the spatiotemporal features learned by the two deep convolutional neural networks. Finally, the fused features are input to the support vector machine to realize the facial expression classification task. The experimental results on cNTERFACE, RML, and AFEW6.0 datasets show that the recognition rates obtained by the proposed method are as high as 88.67%, 70.32%, and 63.84%, respectively. Comparative experiments show that the proposed method obtains higher recognition accuracy than other recently reported methods.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.282-285
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• 2009

This paper proposes a flame verification algorithm using motion and spatial persistency. Most previous vision-based methods using color information and temporal variations of pixels produce frequent false alarms due to the use of many heuristic features. To solve these problems, we used a Bayesian Networks. In addition, since the shape of flame changes upwards irregularly due to the airflow caused by wind or burning material, we distinct real flame from moving objects by checking the motion orientation and temporal persistency of flame regions to remove the misclassification. As a result, the use of two verification steps and a Bayesian inference improved the detection performance and reduced the missing rate.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.212-220
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• 2023

Ontologies are knowledge containers in which information about a specified domain can be shared and reused. An event happens within a specific time and place and in which some actors engage and show specific action features. The fact is that several ontology models are based on events called Event-Based Models, where the event is an individual entity or concept connected with other entities to describe the underlying ontology because the event can be composed of spatiotemporal extents. However, current event-based ontologies are inadequate to bridge the gap between spatiotemporal extents and participants to describe a specific domain event. This paper reviews, describes, and compares the existing event-based ontologies. The paper compares and contrasts various ways of representing the events and how they have been modelled, constructed, and integrated with the ontologies. The primary criterion for comparison is based on the events' ability to represent spatial and temporal extent and the participants in the event.

• 디자인학연구
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• 제18권3호
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• pp.63-74
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• 2005

현대사회는 다원화 사회로서 늑종 영역 또는 어느 분야마다 그 경계가 사라지면서 복잡한 상황 속에 살고 있다. 이러한 복잡한 상황을 폭넓게 이해하고 수용하기 위해서는 개방된 텍스트 구조로서의 상호텍스트적인 환경예술과 수용자와의 상호작용을 이해해야 할 것이다. 상호텍스트적인 특성으로 바라 본 환경예술을 장르와 장르간의 혼합, 이질적 공간과 시간간의 혼합된 요소에 수용자의 체험으로 인한 상호작용을 살펴보았다. 이는 수용자가 예술작품을 완성하는 과정에 참여함으로써 개인적인 경험 또는 상황을 의미하는 개념으로 환상성과 탈 장소성, 장소의 특수성과 시공간의 표현방법을 상호텍스트성의 특성으로 설정하였다. 이러한 체험요소의 특성들을 각 작품의 특징을 분석하는 방법론으로 사용하였다. 환상성의 특성은 이벤트적 상황과 우연성 개입으로 사건이 발생하는 장소를 전개시키며, 탈 장소성은 비 물질적 감각성으로 수용자의 공각각적 체험을 유도하는 전략을 사용하였다. 장소의 맥락을 중요시 한 장소의 특수성, 시공 연속체적 변화를 반영하고 프로세스 위주의 특징으로 시공간 표현방법을 제안하였다. 결과적으로 환경예술은 시각적으로 현존성에 의지하는 형이상학적 장식의 차원을 넘어서 수용자의 복잡한 존재양태에 깊숙이 자리잡고 거기에서부터 삶의 양분을 부단히 공급하는 인식의 전환이 되써야 할 것이다. 그렇다면 환경예술도 일종의 텍스트의 차원에서 다른 모든 텍스트들과 어울어지면서 텍스트적 삶을 살게 될 것이고 창조성은 유일성 대신에 상호텍스트성 사이에서 실천적 창조성으로 다시 태어난다 하겠다. 이러한 타 영역간의 전목, 또 이것을 바라보고 체험하는 수용자들의 다양한 측면을 허용하는 다원적 측면과 끊임없이 새롭게 만들어지는 생성의 개념을 가지는 진행 중인 작품 즉 열린 작품을 만들어내야 할 것이다.

• Molecules and Cells
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• 제43권3호
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• pp.215-221
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• 2020

The olfactory bulb (OB) has an extremely higher proportion of interneurons innervating excitatory neurons than other brain regions, which is evolutionally conserved across species. Despite the abundance of OB interneurons, little is known about the diversification and physiological functions of OB interneurons compared to cortical interneurons. In this review, an overview of the general developmental process of interneurons from the angles of the spatial and temporal specifications was presented. Then, the distinct features shown exclusively in OB interneurons development and molecular machinery recently identified were discussed. Finally, we proposed an evolutionary meaning for the diversity of OB interneurons.

• Journal of Korean Society for Atmospheric Environment
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• 제21권E3호
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• pp.95-105
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• 2005

The concentrations of sulfur dioxide ($SO_{2}$) were measured from seven major cities and nine provinces in Korea for the period covering 1998 to 2003. Its concentration data were analyzed to explore the possible influences of spatial and temporal factors on the $SO_{2}$ distribution characteristics. Examination of spatial trends of $SO_{2}$ distribution and behavior indicated several interesting features. Although its annual trends appeared to be affected by the changes in the surrounding environmental conditions (e.g., regulation on the use of S-containing fuels), the seasonal trends indicated a cyclic and systematic pattern that may be characterized as: a gradual decrease in concentrations across winter, spring, fall, and summer. The results showed the generally enhanced mean concentrations of $SO_{2}$ from Ulsan, Busan, and Daegu with 12.8, 10.1, and 8.80 ppb, respectively. On the other hand, notably reduced $SO_{2}$ concentrations were seen from Gwangju and Jeju sites with its mean values of 5.43 and 3.88 ppb, respectively. The overall results of our study indicate that a decrease in $SO_{2}$ concentration levels continued through time, while its spatial distribution appears to be affected most sensitively by such factor as city scale and industrial activities.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.877-885
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• 2022

Vision-based activity recognition has been widely attempted at construction sites to estimate productivity and enhance workers' health and safety. Previous studies have focused on extracting an individual worker's postural information from sequential image frames for activity recognition. However, various trades of workers perform different tasks with similar postural patterns, which degrades the performance of activity recognition based on postural information. To this end, this research exploited a concept of human-object interaction, the interaction between a worker and their surrounding objects, considering the fact that trade workers interact with a specific object (e.g., working tools or construction materials) relevant to their trades. This research developed an approach to understand the context from sequential image frames based on four features: posture, object, spatial features, and temporal feature. Both posture and object features were used to analyze the interaction between the worker and the target object, and the other two features were used to detect movements from the entire region of image frames in both temporal and spatial domains. The developed approach used convolutional neural networks (CNN) for feature extractors and activity classifiers and long short-term memory (LSTM) was also used as an activity classifier. The developed approach provided an average accuracy of 85.96% for classifying 12 target construction tasks performed by two trades of workers, which was higher than two benchmark models. This experimental result indicated that integrating a concept of the human-object interaction offers great benefits in activity recognition when various trade workers coexist in a scene.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3598-3614
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• 2020

With the increase of motor vehicles and tourism demand, some traffic problems gradually appear, such as traffic congestion, safety accidents and insufficient allocation of traffic resources. Facing these challenges, a model of Spatio-Temporal Dilated Convolutional Network (STDGCN) is proposed for assistance of extracting highly nonlinear and complex characteristics to accurately predict the future traffic flow. In particular, we model the traffic as undirected graphs, on which graph convolutions are built to extract spatial feature informations. Furthermore, a dilated convolution is deployed into graph convolution for capturing multi-scale contextual messages. The proposed STDGCN integrates the dilated convolution into the graph convolution, which realizes the extraction of the spatial and temporal characteristics of traffic flow data, as well as features of road occupancy. To observe the performance of the proposed model, we compare with it with four rivals. We also employ four indicators for evaluation. The experimental results show STDGCN's effectiveness. The prediction accuracy is improved by 17% in comparison with the traditional prediction methods on various real-world traffic datasets.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.978-980
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• 2003

This study explores the temporal and spatial features near 67years (1934 ?2001) and landcover change in last 14 years (1987-2001) in Shijiazhuang, China, based on 67-year time series data edited from historical maps, TM and ETM+ imageries by integrating GIS and remote sensing method. An index named Annual Growth Rate (AGR) is used to analyze the spatial features of urban sprawl, and Maximum Likelihood classification method is utilized to detect the land cover types change. At last, the relationship between urbanization and factors is analyzed.