Repetition Estimation

TL;DR

This paper introduces a theory-based, learning-free method for estimating visual repetition in realistic videos by analyzing periodic motion through spatiotemporal filtering, accommodating non-static and non-stationary conditions.

Contribution

It develops a novel, theory-driven approach that decomposes motion into fundamental components and uses wavelet-filtered motion maps for robust repetition estimation without learning.

Findings

Effective in handling non-static, non-stationary repetitive motion.

Outperforms deep learning methods in repetition counting.

Provides a new dataset reflecting real-world motion complexities.

Abstract

Visual repetition is ubiquitous in our world. It appears in human activity (sports, cooking), animal behavior (a bee's waggle dance), natural phenomena (leaves in the wind) and in urban environments (flashing lights). Estimating visual repetition from realistic video is challenging as periodic motion is rarely perfectly static and stationary. To better deal with realistic video, we elevate the static and stationary assumptions often made by existing work. Our spatiotemporal filtering approach, established on the theory of periodic motion, effectively handles a wide variety of appearances and requires no learning. Starting from motion in 3D we derive three periodic motion types by decomposition of the motion field into its fundamental components. In addition, three temporal motion continuities emerge from the field's temporal dynamics. For the 2D perception of 3D motion we consider the viewpoint relative to the motion; what follows are 18 cases of recurrent motion perception. To estimate repetition under all circumstances, our theory implies constructing a mixture of differential motion maps: gradient, divergence and curl. We temporally convolve the motion maps with wavelet filters to estimate repetitive dynamics. Our method is able to spatially segment repetitive motion directly from the temporal filter responses densely computed over the motion maps. For experimental verification of our claims, we use our novel dataset for repetition estimation, better-reflecting reality with non-static and non-stationary repetitive motion. On the task of repetition counting, we obtain favorable results compared to a deep learning alternative.