The World of Fast Moving Objects
This addresses a specific challenge in video analysis, particularly for sports and other domains, by introducing a new concept and method, but it is incremental in improving localization for a niche problem.
The paper tackles the problem of detecting and tracking Fast Moving Objects (FMOs), which appear as blurred streaks in videos, by proposing a method that recovers object appearance and rotation axis, evaluated on a new dataset showing existing trackers are inadequate.
The notion of a Fast Moving Object (FMO), i.e. an object that moves over a distance exceeding its size within the exposure time, is introduced. FMOs may, and typically do, rotate with high angular speed. FMOs are very common in sports videos, but are not rare elsewhere. In a single frame, such objects are often barely visible and appear as semi-transparent streaks. A method for the detection and tracking of FMOs is proposed. The method consists of three distinct algorithms, which form an efficient localization pipeline that operates successfully in a broad range of conditions. We show that it is possible to recover the appearance of the object and its axis of rotation, despite its blurred appearance. The proposed method is evaluated on a new annotated dataset. The results show that existing trackers are inadequate for the problem of FMO localization and a new approach is required. Two applications of localization, temporal super-resolution and highlighting, are presented.