Η διπλωματική εκπονήθηκε με σκοπό τη δημιουργία προγράμματος που θα έχει ως δεδομένο το συμπιεσμένο βίντεο ενός ποδοσφαιρικού αγώνα και θα επιστρέφει σε μορφή βίντεο τα κυριότερα στιγμιότυπα. Ακόμα υλοποιήθηκαν διάφορες εκδοχές εντοπισμού των βασικών στοιχείων ενός ποδοσφαιρικού αγώνα, όπως οι γραμμές του αγωνιστικού χώρου, η θέση της μπάλας στο γήπεδο ο αριθμός και η πυκνότητα των παιχτών εντός του αγωνιστικού χώρου και η ύπαρξη ή μη του τέρματος εντός ενός frame. Πέραν του εντοπισμού της θέσης της μπάλας, δημιουργήθηκαν και προγράμματα για ball tracking (παρακολούθηση τροχιάς της μπάλας) και σε ειδικές περιπτώσεις, όπως για παράδειγμα σε βίντεο που περιέχει παίχτες με άσπρες φανέλες. Το πρόγραμμα εξαγωγής των φάσεων παίρνει τα παραπάνω στοιχεία που έχουν εντοπιστεί, τα επεξεργάζεται και με κατάλληλο συνδυασμό τους εξάγει συμπέρασμα για το πόσο σημαντικό είναι το εκάστοτε στιγμιότυπο. Πρόκειται, στην ουσία, για ένα είδος αυτόματου μοντάζ που βασίζεται στις τεχνικές της επεξεργασίας εικόνας. / Τhe project in question was accomplished to create an algorithm based on the compressed video of a football match and it will be able to replay the most important events of the match. In addition, several versions of locating the basic elements of a football match were produced, such as the lining of the pitch, the position of the ball on the pitch, the number of players and the players’ pixel density as well as the potential existence of the goalpost within a particular frame. Apart from the identification of the ball's position, additional algorithms of ball tracking were created for special cases, such as videos with players wearing white T-shirts. The highlight detection method relies on the appropriate combination of the previously stated elements (i.e. pitch lining, number of players etc.) and it can finally draw a conclusion on the significance of every moment of the match. In summary, this algorithm can be characterized as "automatic montage", based on the premises of image processing.

It is well understood that multiple video cameras and computer vision (CV) technology can be used in sport for match officiating, statistics and player performance analysis. A review of the literature reveals a number of existing solutions, both commercial and theoretical, within this domain. However, these solutions are expensive and often complex in their installation. The hypothesis for this research states that by considering only changes in ball motion, automatic event classification is achievable with low-cost monocular video recording devices, without the need for 3-dimensional (3D) positional ball data and representation. The focus of this research is a rigorous empirical study of low cost single consumer-grade video camera solutions applied to table tennis, confirming that monocular CV based detected ball location data contains sufficient information to enable key match-play events to be recognised and measured. In total a library of 276 event-based video sequences, using a range of recording hardware, were produced for this research. The research has four key considerations: i) an investigation into an effective recording environment with minimum configuration and calibration, ii) the selection and optimisation of a CV algorithm to detect the ball from the resulting single source video data, iii) validation of the accuracy of the 2-dimensional (2D) CV data for motion change detection, and iv) the data requirements and processing techniques necessary to automatically detect changes in ball motion and match those to match-play events. Throughout the thesis, table tennis has been chosen as the example sport for observational and experimental analysis since it offers a number of specific CV challenges due to the relatively high ball speed (in excess of 100kph) and small ball size (40mm in diameter). Furthermore, the inherent rules of table tennis show potential for a monocular based event classification vision system. As the initial stage, a proposed optimum location and configuration of the single camera is defined. Next, the selection of a CV algorithm is critical in obtaining usable ball motion data. It is shown in this research that segmentation processes vary in their ball detection capabilities and location out-puts, which ultimately affects the ability of automated event detection and decision making solutions. Therefore, a comparison of CV algorithms is necessary to establish confidence in the accuracy of the derived location of the ball. As part of the research, a CV software environment has been developed to allow robust, repeatable and direct comparisons between different CV algorithms. An event based method of evaluating the success of a CV algorithm is proposed. Comparison of CV algorithms is made against the novel Efficacy Metric Set (EMS), producing a measurable Relative Efficacy Index (REI). Within the context of this low cost, single camera ball trajectory and event investigation, experimental results provided show that the Horn-Schunck Optical Flow algorithm, with a REI of 163.5 is the most successful method when compared to a discrete selection of CV detection and extraction techniques gathered from the literature review. Furthermore, evidence based data from the REI also suggests switching to the Canny edge detector (a REI of 186.4) for segmentation of the ball when in close proximity to the net. In addition to and in support of the data generated from the CV software environment, a novel method is presented for producing simultaneous data from 3D marker based recordings, reduced to 2D and compared directly to the CV output to establish comparative time-resolved data for the ball location. It is proposed here that a continuous scale factor, based on the known dimensions of the ball, is incorporated at every frame. Using this method, comparison results show a mean accuracy of 3.01mm when applied to a selection of nineteen video sequences and events. This tolerance is within 10% of the diameter of the ball and accountable by the limits of image resolution. Further experimental results demonstrate the ability to identify a number of match-play events from a monocular image sequence using a combination of the suggested optimum algorithm and ball motion analysis methods. The results show a promising application of 2D based CV processing to match-play event classification with an overall success rate of 95.9%. The majority of failures occur when the ball, during returns and services, is partially occluded by either the player or racket, due to the inherent problem of using a monocular recording device. Finally, the thesis proposes further research and extensions for developing and implementing monocular based CV processing of motion based event analysis and classification in a wider range of applications.

Object tracking seeks to determine the object size and location in the following video frames, given the appearance and location of the object in the first frame. The object tracking approaches can be divided into categories: online trained trackers and offline trained tracker. First group of trackers is based on handcrafted features like HOG or Color Names. This group is characterised by high inference speed, but struggles from lack of highly deterministic features. On the other hand the second group uses Convolution Neural Networks as features extractors. They generate highly meaningful features, but limit the inference speed and possibility of learning object appearance in the offline phase. The following report investigates the problem of tracking a soccer ball on mobile devices. Keeping in mind the limited computational resources of mobile devices, we propose the fused tracker. At the beginning of the video the simple online trained tracker is fired. As soon as the tracker looses the ball, the more advanced tracer, based on deep neural networks is fired. The fusion allows to speed up the inference time, by using the simple tracker as much as possible, but keeps the tracking success rate high, by using the more advanced tracker after the object is lost by the first tracker. Both quantitative and qualitative experiments demonstrate the validity of this approach. / Objektspårning syftar till att bestämma objektets storlek och plats i följande videoramar, med tanke på objektets utseende och plats i den första bilden. Objektspårningsmetoderna kan delas in i kategorier: online-utbildade trackers och offline-utbildade trackers. Första gruppen av trackers är baserad på handgjorda funktioner som HOG eller Color Names. Denna grupp kännetecknas av hög inferenshastighet, men kämpar från brist på mycket deterministiska egenskaper. Å andra sidan använder den andra gruppen Convolution Neural Networks som funktioner för extrahering. De genererar mycket meningsfulla funktioner, men begränsar sluthastigheten och möjligheten att lära sig objekt i offlinefasen. Följande rapport undersöker problemet med att spåra en fotboll på mobila enheter. Med tanke på de begränsade beräkningsresurserna för mobila enheter föreslår vi den smälta trackern. I början av videon sparkas den enkla utbildade spåraren online. Så snart trackern förlorar bollen avfyras den mer avancerade spåraren, baserad på djupa neurala nätverk. Fusionen gör det möjligt att påskynda inferenstiden genom att använda den enkla trackern så mycket som möjligt, men håller spårningsfrekvensen hög, genom att använda den mer avancerade trackern efter att objektet förlorats av den första trackern. Både kvantitativa och kvalitativa experiment visar att detta tillvägagångssätt är giltigt.