Non-Causal Tracking by Deblatting

TL;DR

This paper introduces a non-causal method for tracking motion-blurred objects by deblatting, which estimates continuous trajectories and enables precise physical measurements like velocity and radius, outperforming existing techniques.

Contribution

The paper presents a novel non-causal tracking approach that estimates continuous object trajectories from motion-blurred images, incorporating energy minimization and polynomial fitting for improved accuracy.

Findings

High Trajectory-IoU and recall scores

Accurate velocity estimation compared to high-speed cameras

Effective detection of abrupt motion changes (bounces)

Abstract

Tracking by Deblatting stands for solving an inverse problem of deblurring and image matting for tracking motion-blurred objects. We propose non-causal Tracking by Deblatting which estimates continuous, complete and accurate object trajectories. Energy minimization by dynamic programming is used to detect abrupt changes of motion, called bounces. High-order polynomials are fitted to segments, which are parts of the trajectory separated by bounces. The output is a continuous trajectory function which assigns location for every real-valued time stamp from zero to the number of frames. Additionally, we show that from the trajectory function precise physical calculations are possible, such as radius, gravity or sub-frame object velocity. Velocity estimation is compared to the high-speed camera measurements and radars. Results show high performance of the proposed method in terms of Trajectory-IoU, recall and velocity estimation.