MultiCounter: Multiple Action Agnostic Repetition Counting in Untrimmed Videos
Yin Tang, Wei Luo, Jinrui Zhang, Wei Huang, Ruihai Jing, and Deyu, Zhang

TL;DR
MultiCounter is an end-to-end deep learning framework that accurately detects, tracks, and counts multiple repetitive actions in untrimmed videos, outperforming existing methods and operating in real-time.
Contribution
It introduces novel modules for context correlation and action-agnostic counting, setting new benchmarks in multi-instance repetitive action counting.
Findings
Significantly improves Period-mAP by 41.0%.
Reduces AvgMAE by 58.6%.
Operates in real-time on standard GPU hardware.
Abstract
Multi-instance Repetitive Action Counting (MRAC) aims to estimate the number of repetitive actions performed by multiple instances in untrimmed videos, commonly found in human-centric domains like sports and exercise. In this paper, we propose MultiCounter, a fully end-to-end deep learning framework that enables simultaneous detection, tracking, and counting of repetitive actions of multiple human instances. Specifically, MultiCounter incorporates two novel modules: 1) mixed spatiotemporal interaction for efficient context correlation across consecutive frames, and 2) task-specific heads for accurate perception of periodic boundaries and generalization for action-agnostic human instances. We train MultiCounter on a synthetic dataset called MultiRep generated from annotated real-world videos. Experiments on the MultiRep dataset validate the fundamental challenge of MRAC tasks and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVideo Analysis and Summarization
