TennisSense: a platform for extracting semantic information from multi-camera tennis data

In this paper, we introduce TennisSense, a technology platform for the digital capture, analysis and retrieval of tennis training and matches. Our algorithms for extracting useful metadata from the overhead court camera are described and evaluated. We track the tennis ball using motion images for ball candidate detection and then link ball candidates into locally linear tracks. From these tracks we can infer when serves and rallies take place. Using background subtraction and hysteresis-type blob tracking, we track the tennis players positions. The performance of both modules is evaluated using ground-truthed data. The extracted metadata provides valuable information for indexing and efficient browsing of hours of multi-camera tennis footage and we briefly illustrative how this data is used by our tennis-coach playback interface

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

Three dimensional information estimation and tracking for moving objects detection using two cameras framework

Calibration, matching and tracking are major concerns to obtain 3D information consisting of depth, direction and velocity. In finding depth, camera parameters and matched points are two necessary inputs. Depth, direction and matched points can be achieved accurately if cameras are well calibrated using manual traditional calibration. However, most of the manual traditional calibration methods are inconvenient to use because markers or real size of an object in the real world must be provided or known. Self-calibration can solve the traditional calibration limitation, but not on depth and matched points. Other approaches attempted to match corresponding object using 2D visual information without calibration, but they suffer low matching accuracy under huge perspective distortion. This research focuses on achieving 3D information using self-calibrated tracking system. In this system, matching and tracking are done under self-calibrated condition. There are three contributions introduced in this research to achieve the objectives. Firstly, orientation correction is introduced to obtain better relationship matrices for matching purpose during tracking. Secondly, after having relationship matrices another post-processing method, which is status based matching, is introduced for improving object matching result. This proposed matching algorithm is able to achieve almost 90% of matching rate. Depth is estimated after the status based matching. Thirdly, tracking is done based on x-y coordinates and the estimated depth under self-calibrated condition. Results show that the proposed self-calibrated tracking system successfully differentiates the location of objects even under occlusion in the field of view, and is able to determine the direction and the velocity of multiple moving objects

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

Extracting field hockey player coordinates using a single wide-angle camera

In elite level sport, coaches are always trying to develop tactics to better their opposition. In a team sport such as field hockey, a coach must consider both the strengths and weaknesses of both their own team and that of the opposition to develop an effective tactic. Previous work has shown that spatiotemporal coordinates of the players are a good indicator of team performance, yet the manual extraction of player coordinates is a laborious process that is impractical for a performance analyst. Subsequently, the key motivation of this work was to use a single camera to capture two-dimensional position information for all players on a field hockey pitch. The study developed an algorithm to automatically extract the coordinates of the players on a field hockey pitch using a single wide-angle camera. This is a non-trivial problem that requires: 1. Segmentation and classification of a set of players that are relatively small compared to the image size, and 2. Transformation from image coordinates to world coordinates, considering the effects of the lens distortion due to the wide-angle lens. Subsequently the algorithm addressed these two points in two sub-algorithms: Player Feature Extraction and Reconstruct World Points. Player Feature Extraction used background subtraction to segment player blob candidates in the frame. 61% of blobs in the dataset were correctly segmented, while a further 15% were over-segmented. Subsequently a Convolutional Neural Network was trained to classify the contents of blobs. The classification accuracy on the test set was 85.9%. This was used to eliminate non-player blobs and reform over-segmented blobs. The Reconstruct World Points sub-algorithm transformed the image coordinates into world coordinates. To do so the intrinsic and extrinsic parameters were estimated using planar camera calibration. Traditionally the extrinsic parameters are optimised by minimising the projection error of a set of control points; it was shown that this calibration method is sub-optimal due to the extreme camera pose. Instead the extrinsic parameters were estimated by minimising the world reconstruction error. For a 1:100 scale model the median reconstruction error was 0.0043 m and the distribution of errors had an interquartile range of 0.0025 m. The Acceptable Error Rate, the percentage of points that were reconstructed with less than 0.005 m of error, was found to be 63.5%. The overall accuracy of the algorithm was assessed using the precision and the recall. It found that players could be extracted within 1 m of their ground truth coordinates with a precision of 75% and a recall of 66%. This is a respective improvement of 20% and 16% improvement on the state-of-the-art. However it also found that the likelihood of extraction decreases the further a player is from the camera, reducing to close to zero in parts of the pitch furthest from the camera. These results suggest that the developed algorithm is unsuitable to identify player coordinates in the extreme regions of a full field hockey pitch; however this limitation may be overcome by using multiple collocated cameras focussed on different regions of the pitch. Equally, the algorithm is sport agnostic, so could be used in a sport that uses a smaller pitch

A Novel Multi-View Table Tennis Umpiring Framework

This research investigates the development of a low-cost multi-view umpiring framework, as an alternative to the current expensive systems that are almost exclusively restricted to elite professional sports. Table tennis has been selected as the testbed because, while automating the process is challenging, it has many different complex match elements including the service, return and rallies, which are governed by a strict set of regulations. The focus is mainly on the rally element rather than the whole match. Ball detection and tracking in video frames are undertaken to determine reliably the ball position relative to key reference objects like the table surface and net, and the ball’s flight path is used to determine the rally’s status. While a low-cost option has benefits, it is technically challenging due to the limited number of cameras and generally low video resolution used. This thesis presents a portable multi-view umpiring framework that identifies each state change in a rally. It makes three significant contributions to knowledge: i) a reliable ball detection strategy that accurately detects the location of the ball in low-resolution sequences; ii) a novel framework for ball tracking using a multi-view system, and iii) a new state-machine based evaluation system for analysing table tennis rallies. In a series of ten different test scenarios, the system achieved an average of 94% system detection rate and 100% accurate decisions. A test sequence of duration 1 s can be processed in 8 s, leading to a delay of only 7 s, which is considered acceptable for practical purposes. This solution has the potential to reform the way matches are umpired, providing objectivity in resolving disputed decisions. It affords an economic technology for amateur players, while the multi-view facility is extendible to other relevant ball-based sports. Finally, the ball flight path analysis mechanism can be a valuable training tool for skills development

Measuring Information Security Awareness Efforts in Social Networking Sites – A Proactive Approach

For Social Network Sites to determine the effectiveness of their Information Security Awareness (ISA) techniques, many measurement and evaluation techniques are now in place to ensure controls are working as intended. While these techniques are inexpensive, they are all incident- driven as they are based on the occurrence of incident(s). Additionally, they do not present a true reflection of ISA since cyber-incidents are hardly reported. They are therefore adjudged to be post-mortem and risk permissive, the limitations that are inacceptable in industries where incident tolerance level is low. This paper aims at employing a non-incident statistic approach to measure ISA efforts. Using an object- oriented programming approach, PhP is employed as the coding language with MySQL database engine at the back-end to develop sOcialistOnline – a Social Network Sites (SNS) fully secured with multiple ISA techniques. Rather than evaluating the effectiveness of ISA efforts by success of attacks or occurrence of an event, password scanning is implemented to proactively measure the effects of ISA techniques in sOcialistOnline. Thus, measurement of ISA efforts is shifted from detective and corrective to preventive and anticipatory paradigms which are the best forms of information security approach