提出新方法TR²C，提升复杂动作视频分割精度
Temporal Rate Reduction Clustering for Human Motion Segmentation
- 联合学习时序结构表示与相似性，增强动作片段建模
- 在5个基准数据集上达最优，适配多种特征提取器
- 解决背景杂乱下动作数据偏离子空间分布的问题
人体动作分割（HMS）旨在将视频划分为非重叠的人体动作片段，近年来受到广泛关注。现有方法多基于子空间聚类，依赖高维时间数据服从并集子空间（UoS）分布的假设。然而，包含复杂动作和杂乱背景的视频帧可能不满足UoS分布。本文提出一种新方法Temporal Rate Reduction Clustering（TR²C），联合学习结构化表示与亲和性以分割视频帧序列。所学表示具有时序一致性，且与UoS结构对齐，更利于完成HMS任务。我们在五个基准数据集上进行了广泛实验，使用不同特征提取器均取得当前最佳性能。代码已开源：https://github.com/mengxianghan123/TR2C。
原文摘要 · Abstract (English)
Human Motion Segmentation (HMS), which aims to partition videos into non-overlapping human motions, has attracted increasing research attention recently. Existing approaches for HMS are mainly dominated by subspace clustering methods, which are grounded on the assumption that high-dimensional temporal data align with a Union-of-Subspaces (UoS) distribution. However, the frames in video capturing complex human motions with cluttered backgrounds may not align well with the UoS distribution. In this paper, we propose a novel approach for HMS, named Temporal Rate Reduction Clustering ($\text{TR}^2\text{C}$), which jointly learns structured representations and affinity to segment the sequences of frames in video. Specifically, the structured representations learned by $\text{TR}^2\text{C}$ enjoy temporally consistency and are aligned well with a UoS structure, which is favorable for addressing the HMS task. We conduct extensive experiments on five benchmark HMS datasets and achieve state-of-the-art performances with different feature extractors. The code is available at: https://github.com/mengxianghan123/TR2C.
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