This paper presents a human action recognition method. It analyzes the spatio-temporal grids along the dense trajectories and generates the histogram of oriented gradients （HOG） and histogram of optical flow （HOF）...This paper presents a human action recognition method. It analyzes the spatio-temporal grids along the dense trajectories and generates the histogram of oriented gradients （HOG） and histogram of optical flow （HOF） to describe the appearance and motion of the human object. Then, HOG combined with HOF is converted to bag-of-words （BoWs） by the vocabulary tree. Finally, it applies random forest to recognize the type of human action. In the experiments, KTH database and URADL database are tested for the performance evaluation. Comparing with the other approaches, we show that our approach has a better performance for the action videos with high inter-class and low inter-class variabilities.展开更多
This paper proposes an outdoor guide system using vision-based augmented reality(AR) on mobile devices.Augmented reality provides a virtual-real fusion display interface for outdoor guide.Vision-based methods are more...This paper proposes an outdoor guide system using vision-based augmented reality(AR) on mobile devices.Augmented reality provides a virtual-real fusion display interface for outdoor guide.Vision-based methods are more accurate than GPS or other hardware-based methods.However,vision-based methods require more resources and relatively strong computing power of mobile devices.A C/S framework for vision based augmented reality system is introduced in this paper.In a server,a vocabulary tree is used for location recognition.In a mobile device,BRISK feature is combined with optical flow methods to track the offline keyframe.The system is tested on UKbench datasets and in real environment.Experimental results show that the proposed vision-based augmented reality system works well and yields relatively high recognition rate and that the mobile device achieves realtime recognition performance.展开更多
基金supported by the MOST,Taiwan under Grant No.102-2221-E-468-013
文摘This paper presents a human action recognition method. It analyzes the spatio-temporal grids along the dense trajectories and generates the histogram of oriented gradients （HOG） and histogram of optical flow （HOF） to describe the appearance and motion of the human object. Then, HOG combined with HOF is converted to bag-of-words （BoWs） by the vocabulary tree. Finally, it applies random forest to recognize the type of human action. In the experiments, KTH database and URADL database are tested for the performance evaluation. Comparing with the other approaches, we show that our approach has a better performance for the action videos with high inter-class and low inter-class variabilities.
基金Supported by the National Science and Technology Major Project(No.2012ZX03002004)the National High Technology Research and Development Programme of China(No.2013AA013802)
文摘This paper proposes an outdoor guide system using vision-based augmented reality(AR) on mobile devices.Augmented reality provides a virtual-real fusion display interface for outdoor guide.Vision-based methods are more accurate than GPS or other hardware-based methods.However,vision-based methods require more resources and relatively strong computing power of mobile devices.A C/S framework for vision based augmented reality system is introduced in this paper.In a server,a vocabulary tree is used for location recognition.In a mobile device,BRISK feature is combined with optical flow methods to track the offline keyframe.The system is tested on UKbench datasets and in real environment.Experimental results show that the proposed vision-based augmented reality system works well and yields relatively high recognition rate and that the mobile device achieves realtime recognition performance.
