An automatic visual analysis system for tennis

This article presents a novel video analysis system for coaching tennis players of all levels, which uses computer vision algorithms to automatically edit and index tennis videos into meaningful annotations. Existing tennis coaching software lacks the ability to automatically index a tennis match into key events, and therefore, a coach who uses existing software is burdened with time-consuming manual video editing. This work aims to explore the effectiveness of a system to automatically detect tennis events. A secondary aim of this work is to explore the bene- fits coaches experience in using an event retrieval system to retrieve the automatically indexed events. It was found that automatic event detection can significantly improve the experience of using video feedback as part of an instructional coaching session. In addition to the automatic detection of key tennis events, player and ball movements are automati- cally tracked throughout an entire match and this wealth of data allows users to find interesting patterns in play. Player and ball movement information are integrated with the automatically detected tennis events, and coaches can query the data to retrieve relevant key points during a match or analyse player patterns that need attention. This coaching software system allows coaches to build advanced queries, which cannot be facilitated with existing video coaching solutions, without tedious manual indexing. This article proves that the event detection algorithms in this work can detect the main events in tennis with an average precision and recall of 0.84 and 0.86, respectively, and can typically eliminate man- ual indexing of key tennis events

Review of Person Re-identification Techniques

Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201

A perturbation method for evaluating background subtraction algorithms

We introduce a performance evaluation methodology called Perturbation Detection Rate (PDR) analysis, for measuring performance of background subtraction (BGS) algorithms. It has some advantages over the commonly used Receiver Operation Characteristics (ROC) analysis. Specifically, it does not require foreground targets or knowledge of foreground distributions. It measures the sensitivity of a BGS algorithm in detecting low contrast targets against background as a function of contrast, also depending on how well the model captures mixed (moving) background events. We compare four algorithms having similarities and differences. Three are in [2, 3, 5] while the fourth is recently developed, called Codebook BGS. The latter algorithm quantizes sample background values at each pixel into codebooks which represent a compressed form of background model for a long image sequence

A robust background subtraction and shadow detection

This paper presents a novel algorithm for detecting moving objects from a static background scene that contains shading and shadows using color images. Although the background subtraction technique has been used for years in many vision systems as a preprocessing step for object detection and tracking, most of these algorithms are susceptible to both global and local illumination changes such as shadows and highlights. These cause the consequent processes, e.g. tracking, recognition, etc., to fail. This problem is the underlying motivation of our work. We develop a robust and efficiently computed background subtraction algorithm that is able to cope with local illumination change problems, such as shadows and highlights, as well as global illumination changes. Experimental results, which demonstrate the system’s performance, are also shown. Ã�ÝÛÓÖ�× � segmentation, color model, background subtraction, shadow detection.

Computing 3-D Head Orientation from a Monocular Image Sequence

An approach for estimating 3D head orientation in a monocular image sequence is proposed. The approach employs recently developed image-based parameterized tracking for face and face features to locate the area in which a sub-pixel parameterized shape estimation of the eye's boundary is performed. This involves tracking of five points (four at the eye corners and the fifth is the tip of the nose). We describe an approach that relies on the coarse structure of the face to compute orientation relative to the camera plane. Our approach employs projective invariance of the cross-ratios of the eye corners and anthropometric statistics to estimate the head yaw, roll and pitch. Analytical and experimental results are reported. 1 Introduction We present an algorithm for estimating the orientation of a human face from a single monocular image. The algorithm takes advantage of the geometric symmetries of typical faces to compute the yaw and roll components of orientation, and anthropometric mod..

A statistical approach for real-time robust background subtraction and shadow detection

This paper presents a novel algorithm for detecting moving objects from a static background scene that contains shading and shadows using color images. We develop a robust and e ciently computed background subtraction algorithm that is able to cope with local illumination changes, such as shadows and highlights, as well as global illumination changes. The algorithm is based onaproposed computational color model which separates the brightness from the chromaticity component. We have applied this method toreal image sequences of both indoor and outdoor scenes. The results, which demonstrate the system's performance, and some speed uptechniques we employed in our implementation are also shown. 1

An Anthropometric Shape Model For Estimating Head Orientation

An approach for estimating 3D head orientation in a monocular image sequence is presented. The approach employs recently developed imagebased parameterized tracking for face and face features to locate the area in which an estimation of point feature locations is performed. This involves tracking of five points (four at the eye corners and the fifth is the tip of the nose). We describe an approach that relies on the coarse structure of the face to compute orientation relative to the camera plane. Our approach employs symmetry of the eyes and anthropometric statistics to estimate the head yaw, roll and pitch. Keywords: Face orientation, feature tracking, anthropometry. 1 Introduction Watching people move is a favorite human pastime, and the shapes of people and their parts play important roles in the lives of both human and computer programs. Human shape is dynamic, due to the many degrees of articulative freedom of the human body, and the deformations of the body and its par..