The goal of Online Action Detection (OAD) is to detect action in a timely manner and to recognize its action category. Early works focused on early action detection, which is effectively formulated as a classification problem instead of online detection in streaming videos, because these works used partially seen short video clip that begins at the start of action. Recently, researchers started to tackle the OAD problem in the challenging setting of untrimmed, streaming videos that contain substantial background shots. However, they evaluate OAD in terms of per-frame labeling, which does not require detection at the instance-level and does not evaluate the timeliness of the online detection process. In this paper, we design new protocols and metrics. Further, to specifically address challenges of OAD in untrimmed, streaming videos, we propose three novel methods: (1) we design a hard negative samples generation module based on Generative Adversarial Network (GAN) framework to better distinguish ambiguous background shots that share similar scenes but lack true characteristics of action start; (2) during training we impose a temporal consistency constraint between data around action start and data succeeding action start to model their similarity; (3) we introduce an adaptive sampling strategy to handle the scarcity of the important training data around action start. We conduct extensive experiments using THUMOS'14 and ActivityNet. We show that our proposed strategies lead to significant performance gains and improve state-of-the-art results. A systematic ablation study also confirms the effectiveness of each proposed method.

该论文探讨了在线检测未修剪视频中动作开始的新颖任务-在线开始检测（ODAS）的技术，提出了三种方法来解决ODAS模型训练中存在的挑战，包括利用生成对抗网络（GAN）产生艰难的负样本、显式建模动作开始周围数据和后续数据之间的时态一致性以及自适应抽样策略来处理训练数据的稀缺性，进而在THUMOS'14和ActivityNet上进行广泛实验，证明了提出的方法的显著性能提升和改善先前最先进方法的有效性。

在线检测未剪辑流视频中的动作开始