Existing approaches for spatio-temporal action detection in videos are
limited by the spatial extent and temporal duration of the actions. In this
paper, we present a modular system for spatio-temporal action detection in
untrimmed security videos. We propose a two stage approach. The first stage
generates dense spatio-temporal proposals using hierarchical clustering and
temporal jittering techniques on frame-wise object detections. The second stage
is a Temporal Refinement I3D (TRI-3D) network that performs action
classification and temporal refinement on the generated proposals. The object
detection-based proposal generation step helps in detecting actions occurring
in a small spatial region of a video frame, while temporal jittering and
refinement helps in detecting actions of variable lengths. Experimental results
on the spatio-temporal action detection dataset - DIVA - show the effectiveness
of our system. For comparison, the performance of our system is also evaluated
on the THUMOS14 temporal action detection dataset.

本文提出了一种基于对象检测的两阶段模块化系统，用于在未裁剪的安全视频中进行时空动作检测，第一阶段使用层次聚类和时间抖动技术生成稠密的时空建议，第二阶段是一个 TRI-3D 网络，在生成的提议上执行动作分类和时态细化。实验结果表明本文提出的方法在区分不同时长、空间尺度范围内的动作检测方面效果显著。