Energy-based Periodicity Mining with Deep Features for Action Repetition Counting in Unconstrained Videos

TL;DR

This paper introduces a novel energy-based periodicity mining method using deep features for counting action repetitions in unconstrained videos, eliminating preprocessing and handling arbitrary periodic actions effectively.

Contribution

It presents a new approach that leverages deep features and high-energy rules for periodicity detection, enabling accurate counting without preprocessing for any repetitive action.

Findings

Achieves comparable results on YT Segments and QUVA datasets.

Effectively handles arbitrary periodicity actions.

No preprocessing required, suitable for real applications.

Abstract

Action repetition counting is to estimate the occurrence times of the repetitive motion in one action, which is a relatively new, important but challenging measurement problem. To solve this problem, we propose a new method superior to the traditional ways in two aspects, without preprocessing and applicable for arbitrary periodicity actions. Without preprocessing, the proposed model makes our method convenient for real applications; processing the arbitrary periodicity action makes our model more suitable for the actual circumstance. In terms of methodology, firstly, we analyze the movement patterns of the repetitive actions based on the spatial and temporal features of actions extracted by deep ConvNets; Secondly, the Principal Component Analysis algorithm is used to generate the intuitive periodic information from the chaotic high-dimensional deep features; Thirdly, the periodicity is mined based on the high-energy rule using Fourier transform; Finally, the inverse Fourier transform with a multi-stage threshold filter is proposed to improve the quality of the mined periodicity, and peak detection is introduced to finish the repetition counting. Our work features two-fold: 1) An important insight that deep features extracted for action recognition can well model the self-similarity periodicity of the repetitive action is presented. 2) A high-energy based periodicity mining rule using deep features is presented, which can process arbitrary actions without preprocessing. Experimental results show that our method achieves comparable results on the public datasets YT Segments and QUVA.