The World of Fast Moving Objects

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.

References in corpus (3)

• Learning Spatially Regularized Correlation Filters for Visual Tracking
• A Novel Performance Evaluation Methodology for Single-Target Trackers
• Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal

Cited by in corpus (10)

• DeFMO: Deblurring and Shape Recovery of Fast Moving Objects
• Aggregation Signature for Small Object Tracking
• Intra-frame Object Tracking by Deblatting
• Sub-frame Appearance and 6D Pose Estimation of Fast Moving Objects
• Non-Causal Tracking by Deblatting
• FMODetect: Robust Detection of Fast Moving Objects
• Fast Kernelized Correlation Filters without Boundary Effect
• Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects
• A Tracking System For Baseball Game Reconstruction
• Learning-based Tracking of Fast Moving Objects