This paper seeks to envision future architectural planning as it relates to living with robots and to clarify the research theme for it. Living with robots is no longer a fantasy seen just on TV or in movies. The conc...This paper seeks to envision future architectural planning as it relates to living with robots and to clarify the research theme for it. Living with robots is no longer a fantasy seen just on TV or in movies. The concept of the intelligent space emerged from the field of robotics in the mid-1990s. The idea of the intelligent space is that the robot will support the architecture and users of the space by keeping in close contact with architectural space equipped with intelligent technologies. The benefit of the intelligent space is that the robot can be smaller and less intelligent because the robot will be assisted by advanced technologies embedded in the architectural space, such as sensors and actuators. Therefore, the robot can consequently assist user＇s activities with delicate care. This paper describes the vision and possibility for architectural planning as it relates to live with robots who can support the inhabitants＇ lives. This paper introduces the study of the relationship between humans and moving robot in architectural space, especially support region for humans by desktop mobile robot.展开更多
Real-time Human action classification in complex scenes has applications in various domains such as visual surveillance, video retrieval and human robot interaction. While, the task is challenging due to computation e...Real-time Human action classification in complex scenes has applications in various domains such as visual surveillance, video retrieval and human robot interaction. While, the task is challenging due to computation efficiency, cluttered backgrounds and intro-variability among same type of actions. Spatio-temporal interest point （STIP） based methods have shown promising results to tackle human action classification in complex scenes efficiently. However, the state-of-the-art works typically utilize bag-of-visual words （BoVW） model which only focuses on the word distribution of STIPs and ignore the distinctive character of word structure. In this paper, the distribution of STIPs is organized into a salient directed graph, which reflects salient motions and can be divided into a time salient directed graph and a space salient directed graph, aiming at adding spatio-temporal discriminant to BoVW. Generally speaking, both salient directed graphs are constructed by labeled STIPs in pairs. In detail, the ＂directional co-occurrence＂ property of different labeled pairwise STIPs in same frame is utilized to represent the time saliency, and the space saliency is reflected by the ＂geometric relationships＂ between same labeled pairwise STIPs across different frames. Then, new statistical features namely the Time Salient Pairwise feature （TSP） and the Space Salient Pairwise feature （SSP） are designed to describe two salient directed graphs, respectively. Experiments are carried out with a homogeneous kernel SVM classifier, on four challenging datasets KTH, ADL and UT-Interaction. Final results confirm the complementary of TSP and SSP, and our multi-cue representation TSP ＋ SSP ＋ BoVW can properly describe human actions with large intro-variability in real-time.展开更多
文摘This paper seeks to envision future architectural planning as it relates to living with robots and to clarify the research theme for it. Living with robots is no longer a fantasy seen just on TV or in movies. The concept of the intelligent space emerged from the field of robotics in the mid-1990s. The idea of the intelligent space is that the robot will support the architecture and users of the space by keeping in close contact with architectural space equipped with intelligent technologies. The benefit of the intelligent space is that the robot can be smaller and less intelligent because the robot will be assisted by advanced technologies embedded in the architectural space, such as sensors and actuators. Therefore, the robot can consequently assist user＇s activities with delicate care. This paper describes the vision and possibility for architectural planning as it relates to live with robots who can support the inhabitants＇ lives. This paper introduces the study of the relationship between humans and moving robot in architectural space, especially support region for humans by desktop mobile robot.
基金This work is supported by the National Natural Science Foundation of China （NSFC, nos. 61340046）, the National High Technology Research and Development Programme of China （863 Programme, no. 2006AA04Z247）, the Scientific and Technical Innovation Commission of Shenzhen Munici-pality （nos. JCYJ20130331144631730）, and the Specialized Research Fund for the Doctoral Programme of Higher Edu- cation （SRFDP, no. 20130001110011）.
文摘Real-time Human action classification in complex scenes has applications in various domains such as visual surveillance, video retrieval and human robot interaction. While, the task is challenging due to computation efficiency, cluttered backgrounds and intro-variability among same type of actions. Spatio-temporal interest point （STIP） based methods have shown promising results to tackle human action classification in complex scenes efficiently. However, the state-of-the-art works typically utilize bag-of-visual words （BoVW） model which only focuses on the word distribution of STIPs and ignore the distinctive character of word structure. In this paper, the distribution of STIPs is organized into a salient directed graph, which reflects salient motions and can be divided into a time salient directed graph and a space salient directed graph, aiming at adding spatio-temporal discriminant to BoVW. Generally speaking, both salient directed graphs are constructed by labeled STIPs in pairs. In detail, the ＂directional co-occurrence＂ property of different labeled pairwise STIPs in same frame is utilized to represent the time saliency, and the space saliency is reflected by the ＂geometric relationships＂ between same labeled pairwise STIPs across different frames. Then, new statistical features namely the Time Salient Pairwise feature （TSP） and the Space Salient Pairwise feature （SSP） are designed to describe two salient directed graphs, respectively. Experiments are carried out with a homogeneous kernel SVM classifier, on four challenging datasets KTH, ADL and UT-Interaction. Final results confirm the complementary of TSP and SSP, and our multi-cue representation TSP ＋ SSP ＋ BoVW can properly describe human actions with large intro-variability in real-time.
