A Comparative Analysis Using Silhouette Extraction Methods for Dynamic Objects in Monocular Vision

Abstract

Moving or dynamic object analysis continues to be an increasingly active research field in computer vision, with many types of research investigating different methods for motion tracking, object recognition, pose estimation, or motion evaluation (e.g., in sports sciences). Many techniques are available to measure the forces and motion of people, such as force plates to measure ground reaction forces for jumping or running sports. In training and commercial solutions, the detailed motion of an athlete is captured using motion capture devices based on optical markers on the athlete's body and multiple calibrated fixed cameras around the sides of the capture volume. In some situations, it is not practical to attach any kind of marker or transducer to the athletes, or the existing machinery is being used, making it necessary to use a pure vision-based approach that relies on the natural appearance of the person or object. When a sporting event is taking place, there are opportunities for computer vision to help the referee and other personnel involved in the sports to keep track of incidents occurring, which may provide full coverage and detailed analysis of the event for sports viewers. The research aims at using computer vision methods, specially designed for monocular recording, for measuring sports activities, such as high jump, wide jump, or running. To indicate the complexity of the project: a single camera needs to understand the height at a particular distance using silhouette extraction. Moving object analysis benefits from silhouette extraction, and this has been applied to many domains, including sports activities. This paper comparatively discusses two significant techniques to extract silhouettes of a moving object (a jumping person) in monocular video data in different scenarios. The results show that the performance of silhouette extraction varies depending on the quality of the used video data.