论文标题
在未修剪视频中重新思考在线操作检测：一种新颖的在线评估协议
Rethinking Online Action Detection in Untrimmed Videos: A Novel Online Evaluation Protocol
论文作者
论文摘要
需要重新审视在线操作检测（OAD）问题。与传统的离线行动检测方法不同，评估指标清晰且建立了良好，在OAD环境中，我们发现的作品很少，并且对要使用的评估协议没有共识。在这项工作中，我们建议重新考虑OAD场景，清楚地定义问题本身以及被认为在线的模型必须遵守的主要特征。我们还引入了一个新颖的指标：瞬时准确性（$ ia $）。这个新的指标表现出\ emph {在线}性质，并解决了以前指标的大部分局限性。我们对3个具有挑战性的数据集进行了彻底的实验评估，其中将各种基线方法的性能与最先进的方法进行了比较。我们的结果证实了先前评估协议的问题，并表明基于IA的协议更适合在线方案。公开可用的基线模型和具有新评估协议的开发套件：https：//github.com/gramuah/ia。
The Online Action Detection (OAD) problem needs to be revisited. Unlike traditional offline action detection approaches, where the evaluation metrics are clear and well established, in the OAD setting we find very few works and no consensus on the evaluation protocols to be used. In this work we propose to rethink the OAD scenario, clearly defining the problem itself and the main characteristics that the models which are considered online must comply with. We also introduce a novel metric: the Instantaneous Accuracy ($IA$). This new metric exhibits an \emph{online} nature and solves most of the limitations of the previous metrics. We conduct a thorough experimental evaluation on 3 challenging datasets, where the performance of various baseline methods is compared to that of the state-of-the-art. Our results confirm the problems of the previous evaluation protocols, and suggest that an IA-based protocol is more adequate to the online scenario. The baselines models and a development kit with the novel evaluation protocol are publicly available: https://github.com/gramuah/ia.
