Video action recognition has been a challenging task over the years. The challenge herein is not only due to the complication in increasing information in videos but also the requirement of an efficient method to retain information over a longer-term where human action would take to perform. This paper proposes a novel framework, named as long-term video action recognition (LVAR) to perform generic action classification in the continuous video. The idea of LVAR is introducing a partial recurrence connection to propagate information within every layer of a spatial-temporal network, such as the well-known C3D. Empirically, we show that this addition allows the C3D network to access long-term information, and subsequently improves action recognition performance with videos of different length selected from both UCF101 and miniKinetics datasets. Further confirmation of our approach is strengthened with experiments on untrimmed video from the Thumos14 dataset.

多年来，视频动作识别一直是一项艰巨的任务。这里的挑战不仅是由于视频中信息增加的复杂性，还在于需要一种有效的方法来长期保留信息，而人类将采取这种行动来执行此任务。本文提出了一个新颖的框架，称为长期视频动作识别（LVAR）在连续视频中执行通用动作分类。LVAR的思想是引入部分递归连接以在时空网络（例如众所周知的C3D）的每一层内传播信息。从经验上看，我们表明此添加项使C3D网络可以访问长期信息，并随后使用从UCF101和miniKinetics数据集中选择的不同长度的视频提高了动作识别性能。我们对Thumos14数据集的未修剪视频进行了实验，进一步证实了我们的方法。