The World of Fast Moving Objects

TL;DR

This paper introduces the concept of Fast Moving Objects (FMOs), proposes a three-step detection and tracking method, and demonstrates its effectiveness on a new dataset, enabling applications like super-resolution and highlighting.

Contribution

It presents a novel FMO detection and tracking pipeline capable of recovering object appearance and rotation despite motion blur, addressing limitations of existing trackers.

Findings

The proposed method successfully localizes FMOs in various conditions.

Existing trackers are inadequate for FMO localization.

Applications include temporal super-resolution and object highlighting.

Abstract

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.