2015年THUMOS挑战赛数据集，野外视频中自动识别和定位大量的动作类别

Action Recognition in Temporally Untrimmed Videos! Automatically recognizing and localizing a large number of action categories from videos in the wild of significant importance for video understanding and multimedia event detection. THUMOS workshop and challenge aims at exploring new challenges and approaches for large-scale action recognition with large number of classes from open source videos in a realistic setting. Most of the existing action recognition datasets are composed of videos that have been manually trimmed to bound the action of interest. This has been identified to be a considerable limitation as it poorly matches how action recognition is applied in practical settings. Therefore, THUMOS 2015 will conduct the challenge on temporally untrimmed videos. The participants may train their methods using trimmed clips but will be required to test their systems on untrimmed data. A new forward-looking dataset containing over 430 hours of video data and 45 million frames (70% larger than THUMOS'14) with the following components is made available under this challenge: All videos are collected from YouTube. We will evaluate the success of the proposed methods based on their performance on the new THUMOS 2015 Dataset in two tasks: Participants may either submit a notebook paper that briefly describes their system, or a research paper detailing their approach. All of the submission results will be summarized during the workshop and included in the workshopconference proceedings. Additionally, the top performers will be invited to give oral presentations, with remaining entries encouraged to present their work in the poster session. For more details, please see the evaluation Setup document or the released resources.

时序未剪辑视频中的动作识别（Action Recognition in Temporally Untrimmed Videos）！从真实世界场景下的视频中自动识别并定位大量动作类别，这对于视频理解与多媒体事件检测具有重要意义。THUMOS 研讨会与挑战赛旨在探索面向大规模动作识别的全新挑战与解决方案，该任务需从真实场景下的开源视频中识别大量动作类别。现有多数动作识别数据集均由人工剪辑、仅保留目标动作片段的视频构成，这一做法存在显著局限，因为其与实际场景中动作识别的应用方式严重不符。因此，THUMOS 2015 将针对时序未剪辑视频开展本次挑战赛。参赛选手可使用剪辑后的片段训练模型，但需在未剪辑数据上测试系统性能。本次挑战赛将推出一款前瞻性数据集，该数据集包含超过430小时的视频数据与4500万帧图像，体量较THUMOS'14扩大70%，所有视频均采集自YouTube。我们将基于参赛方法在全新的THUMOS 2015 数据集上的表现，从两项任务维度评估方案的优劣：参赛选手可提交简要描述系统的短文（Notebook Paper），或详述研究方法的正式研究论文。所有提交的参赛结果将在研讨会期间汇总，并收录至研讨会暨会议论文集中。此外，表现优异的参赛队伍将受邀进行口头报告，其余参赛作品可选择在海报展示环节展示研究成果。欲了解更多细节，请参阅评估方案文档或已发布的相关资源。