Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes

In this paper we address the problem of multiple camera calibration in the presence of a homogeneous scene, and without the possibility of employing calibration object based methods. The proposed solution exploits salient features present in a larger field of view, but instead of employing active vision we replace the cameras with stereo rigs featuring a long focal analysis camera, as well as a short focal registration camera. Thus, we are able to propose an accurate solution which does not require intrinsic variation models as in the case of zooming cameras. Moreover, the availability of the two views simultaneously in each rig allows for pose re-estimation between rigs as often as necessary. The algorithm has been successfully validated in an indoor setting, as well as on a difficult scene featuring a highly dense pilgrim crowd in Makkah.Comment: 13 pages, 6 figures, submitted to Machine Vision and Application

3D-TV Production from Conventional Cameras for Sports Broadcast

3DTV production of live sports events presents a challenging problem involving conflicting requirements of main- taining broadcast stereo picture quality with practical problems in developing robust systems for cost effective deployment. In this paper we propose an alternative approach to stereo production in sports events using the conventional monocular broadcast cameras for 3D reconstruction of the event and subsequent stereo rendering. This approach has the potential advantage over stereo camera rigs of recovering full scene depth, allowing inter-ocular distance and convergence to be adapted according to the requirements of the target display and enabling stereo coverage from both existing and ‘virtual’ camera positions without additional cameras. A prototype system is presented with results of sports TV production trials for rendering of stereo and free-viewpoint video sequences of soccer and rugby

Application of augmented reality and robotic technology in broadcasting: A survey

As an innovation technique, Augmented Reality (AR) has been gradually deployed in the broadcast, videography and cinematography industries. Virtual graphics generated by AR are dynamic and overlap on the surface of the environment so that the original appearance can be greatly enhanced in comparison with traditional broadcasting. In addition, AR enables broadcasters to interact with augmented virtual 3D models on a broadcasting scene in order to enhance the performance of broadcasting. Recently, advanced robotic technologies have been deployed in a camera shooting system to create a robotic cameraman so that the performance of AR broadcasting could be further improved, which is highlighted in the paper

Towards Efficient Ice Surface Localization From Hockey Broadcast Video

Using computer vision-based technology in ice hockey has recently been embraced as it allows for the automatic collection of analytics. This data would be too expensive and time-consuming to otherwise collect manually. The insights gained from these analytics allow for a more in-depth understanding of the game, which can influence coaching and management decisions. A fundamental component of automatically deriving analytics from hockey broadcast video is ice rink localization. In broadcast video of hockey games, the camera pans, tilts, and zooms to follow the play. To compensate for this motion and get the absolute locations of the players and puck on the ice, an ice rink localization pipeline must find the perspective transform that maps each frame to an overhead view of the rink. The lack of publicly available datasets makes it difficult to perform research into ice rink localization. A novel annotation tool and dataset are presented, which includes 7,721 frames from National Hockey League game broadcasts. Since ice rink localization is a component of a full hockey analytics pipeline, it is important that these methods be as efficient as possible to reduce the run time. Small neural networks that reduce inference time while maintaining high accuracy can be used as an intermediate step to perform ice rink localization by segmenting the lines from the playing surface. Ice rink localization methods tend to infer the camera calibration of each frame in a broadcast sequence individually. This results in perturbations in the output of the pipeline, as there is no consideration of the camera calibrations of the frames before and after in the sequence. One way to reduce the noise in the output is to add a post-processing step after the ice has been localized to smooth the camera parameters and closely simulate the camera’s motion. Several methods for extracting the pan, tilt, and zoom from the perspective transform matrix are explored. The camera parameters obtained from the inferred perspective transform can be smoothed to give a visually coherent video output. Deep neural networks have allowed for the development of architectures that can perform several tasks at once. A basis for networks that can regress the ice rink localization parameters and simultaneously smooth them is presented. This research provides several approaches for improving ice rink localization methods. Specifically, the analytics pipelines can become faster and provide better results visually. This can allow for improved insight into hockey games, which can increase the performance of the hockey team with reduced cost

Virtual camera synthesis for soccer game replays

International audienceIn this paper, we present a set of tools developed during the creation of a platform that allows the automatic generation of virtual views in a live soccer game production. Observing the scene through a multi-camera system, a 3D approximation of the players is computed and used for the synthesis of virtual views. The system is suitable both for static scenes, to create bullet time effects, and for video applications, where the virtual camera moves as the game plays

Storytelling with salient stills

Thesis (M.S.)--Massachusetts Institute of Technology, Program in Media Arts & Sciences, 1996.Includes bibliographical references (p. 59-63).Michale J. Massey.M.S

System for automatic aircraft observation and recording based on ADS-B

Bakalárska práca popisuje vývoj systému určeného na automatické sledovanie a nahrávanie lietadiel využitím systému ADS-B. Systém spracuváva ADS-B správy vysielané lietadlami použitím vhodného prijímača a zobrazuje ich na mape dovoľujúc používateľovi si vybrať lietadlo pomocou zabudovanej dotykovej obrazovky, ktoré by rád zachytil na video. Ďalej systém zisťuje svoju vlastnú polohu na Zemi pomocou GPS systému a magnetickú orientáciu potrebnú pre úspešné sledovanie. Po tom ako si užívateľ vyberie stroj ktorý chce zaznamenať, servomotory sa snažia plynule a presne hýbať kamerou tak aby objekt bol zachytený vo video súbore. Platforma je napájaná batériami a namontovaná na plastovú dosku, ktorá sa dá pripevniť na statív---systém má byť použiteľný na akomkoľvek mieste.The thesis describes the development of a system for automatic aircraft observation and recording using ADS-B surveillance system. The system process ADS-B messages acquired from aircrafts using suitable receiver and plots them on the map, allowing user to choice the plane he would like to capture through the built-in touchscreen. Furthermore it determines its own position on the Earth with GPS and its magnetic heading required for successful aircraft tracking. After selecting the aircraft desired to watch, servomotors try to smoothly and precisely move the camera so the object is recorder to the video file. The system is powered by batteries and is assembled to the mobile plastic board capable of mounting to a tripod---to allow the system be used on any place

System management algorithms for distributed vision networks

[no abstract