This paper presents a new approach for temporal detection of short human activities in untrimmed videos. Most present methods for temporal action detection, to our best knowledge, are trained on public action datasets that feature actions spanning up to tens and hundreds of seconds. However, it is often desired in manufacturing, transportation, and other safety-critical scenes that fine-grained actions be automatically detected, classified, and monitored. We propose a new Dilated Convolutional Temporal Prediction Network that features 1-D dilated convolution operation in a Residual network (ResNet)-like architecture for the generation of action proposals on orders of fractions of a second. The new architecture is used as a part of the action monitoring pipeline in subway cars. Experiments demonstrate that the proposed model outperforms the state-of-the-art on the task of temporal action proposal generation on a real-world video dataset, while achieving a fast processing speed suitable for online monitoring.

本文提出了一种新的方法，用于对未修剪视频中的短暂人类活动进行时间检测。据我们所知，目前大多数用于时间动作检测的方法都是在公共动作数据集上进行训练的，这些数据集的动作长达数十秒和数百秒。但是，在制造，运输和其他对安全要求严格的场景中，通常希望自动检测，分类和监视细粒度的动作。我们提出了一种新的扩散卷积时间预测网络，该网络在类似残留网络（ResNet）的体系结构中具有1-D扩散卷积操作，可在几分之一秒的时间内生成动作建议。新的体系结构被用作地铁车厢行为监控管道的一部分。