论文标题
通过因果表示在线时空行动检测和预测
Online Spatiotemporal Action Detection and Prediction via Causal Representations
论文作者
论文摘要
在本文中，我们将重点从在线和实时处理的角度理解视频动作理解问题。我们首先将传统的离线时空动作检测管道转换为在线时空动作管检测系统。动作管是一组随着时间连接的边界，它在空间和时间上界定了一个动作实例。接下来，我们通过通过回归将现有的动作管扩展到未来，探索这种检测方法的未来预测能力。稍后，我们试图确定在线/因果关系可以实现与离线三维（3D）卷积神经网络（CNN）的相似性能，包括行动识别，时间动作细分和早期预测。
In this thesis, we focus on video action understanding problems from an online and real-time processing point of view. We start with the conversion of the traditional offline spatiotemporal action detection pipeline into an online spatiotemporal action tube detection system. An action tube is a set of bounding connected over time, which bounds an action instance in space and time. Next, we explore the future prediction capabilities of such detection methods by extending an existing action tube into the future by regression. Later, we seek to establish that online/causal representations can achieve similar performance to that of offline three dimensional (3D) convolutional neural networks (CNNs) on various tasks, including action recognition, temporal action segmentation and early prediction.
