太极:一个细粒度动作识别数据集

Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval Pub Date : 2017-06-06 DOI:10.1145/3078971.3079039

Shan Sun, Feng Wang, Qi Liang, Liang He

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引用次数: 9

摘要

本文介绍了一个细粒度动作数据集太极。它由不受约束的用户上传的包含摄像机运动和部分遮挡的网络视频组成，与现有数据集相比，这对细粒度动作识别提出了新的挑战。在这个数据集中，58个细粒度动作类的2772个样本被手工标注。此外，我们使用最先进的改进密集轨迹特征和Fisher向量表示提供基线动作识别结果，MAP(平均精度)为51.39%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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TaiChi: A Fine-Grained Action Recognition Dataset

In this paper, we introduce TaiChi which is a fine-grained action dataset. It consists of unconstrained user-uploaded web videos containing camera motion and partial occlusions which pose new challenges to fine-grained action recognition compared to the existing datasets. In this dataset, 2,772 samples of 58 fine-grained action classes are manually annotated. Additionally, we provide the baseline action recognition results using the state-of-the-art Improved Dense Trajectory feature and Fisher Vector representation with an MAP (Mean Average Precision) of 51.39%.

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Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval

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