来自亚平宁 迪拜VQ Ventiquattro Radisson SAS residence酒店

Matteo Nunziati是一位年轻的意大利设计师,最近几年他在室内设计,尤其是酒店设计方面崭露头角,获得了很多关注。最近几年,他的项目大多在阿联酋,毫无疑问,这里的机会要比在意大利本国多得多。     

Matching Trajectories between Video Sequences by Exploiting a Sparse Projective Invariant Representation

Identifying correspondences between trajectory segments observed from nonsynchronized cameras is important for reconstruction of the complete trajectory of moving targets in a large scene. Such a reconstruction can be obtained from motion data by comparing the trajectory segments and estimating both the spatial and temporal alignments. Exhaustive testing of all possible correspondences of trajectories over a temporal window is only viable in the cases with a limited number of moving targets and large view overlaps. Therefore, alternative solutions are required for situations with several trajectories that are only partially visible in each view. In this paper, we propose a new method that is based on view-invariant representation of trajectories, which is used to produce a sparse set of salient points for trajectory segments observed in each view. Only the neighborhoods at these salient points in the view--invariant representation are then used to estimate the spatial and temporal alignment of trajectory pairs in different views. It is demonstrated that, for planar scenes, the method is able to recover with good precision and efficiency both spatial and temporal alignments, even given relatively small overlap between views and arbitrary (unknown) temporal shifts of the cameras. The method also provides the same capabilities in the case of trajectories that are only locally planar, but exhibit some nonplanarity at a global level.     

Highlights modeling and detection in sports videos

Automatic annotation of semantic events allows effective retrieval of video content. In this work, we present solutions for highlights detection in sports videos. This application is particularly interesting for broadcasters, since they extensively use manual annotation to select interesting highlights that are edited to create new programmes. The proposed approach exploits the typical structure of a wide class of sports videos, namely, those related to sports which are played in delimited venues with playfields of well known geometry, like soccer, basketball, swimming, track and field disciplines, and so on. For this class of sports, a modeling scheme based on a limited set of visual cues and on finite state machines (FSM) that encode the temporal evolution of highlights is presented. Algorithms for model checking and for visual cues estimation are discussed, as well as applications of the representation to different sport domains.     

Semantic annotation of soccer videos: automatic highlights identification

Automatic semantic annotation of video streams allows both to extract significant clips for production logging and to index video streams for posterity logging. Automatic annotation for production logging is particularly demanding, as it is applied to non-edited video streams and must rely only on visual information. Moreover, annotation must be computed in quasi real-time. In this paper, we present a system that performs automatic annotation of the principal highlights in soccer video, suited for both production and posterity logging. The knowledge of the soccer domain is encoded into a set of finite state machines, each of which models a specific highlight. Highlight detection exploits visual cues that are estimated from the video stream, and particularly, ball motion, the currently framed playfield zone, players’ positions and colors of players’ uniforms. The highlight models are checked against the current observations, using a model checking algorithm. The system has been developed within the EU ASSAVID project.     

Common Visual Cues for Sports Highlights Modeling

Automatic annotation of semantic events allows effective retrieval of video content. In this work, we present solutions for highlights detection in sports videos. The proposed approach exploits the typical structure of a wide class of sports videos, namely those related to sports which are played in delimited venues with playfields of well known geometry, like soccer, basketball, swimming, track and field disciplines, and so on. For these sports, a modeling scheme based on a limited set of visual cues and on finite state machines that encode the temporal evolution of highlights is presented, that is of general applicability to this class of sports. Visual cues encode position and speed information coming from the camera and from the object/athletes that are present in the scene, and are estimated automatically from the video stream. Algorithms for model checking and for visual cues estimation are discussed, as well as applications of the representation to different sport domains.