This paper proposes motion prediction in single still images by learning it
from a set of videos. The building assumption is that similar motion is
characterized by similar appearance. The proposed method learns local motion
patterns given a specific appearance and adds the predicted motion in a number
of applications. This work (i) introduces a novel method to predict motion from
appearance in a single static image, (ii) to that end, extends of the
Structured Random Forest with regression derived from first principles, and
(iii) shows the value of adding motion predictions in different tasks such as:
weak frame-proposals containing unexpected events, action recognition, motion
saliency. Illustrative results indicate that motion prediction is not only
feasible, but also provides valuable information for a number of applications.

该论文提出了一种新的方法，通过在一组视频中学习，从单个静态图像中预测运动，该方法利用结构随机森林和回归来学习局部运动模式，并在多种应用程序中添加预测的运动，例如弱帧提议、动作识别和运动显著性。研究表明，运动预测不仅可行，而且对多种应用程序提供有价值的信息。

静态图像中的运动预测

Deja Vu: Motion Prediction in Static Images

We address the problem of action detection in videos. Driven by the latest
progress in object detection from 2D images, we build action models using rich
feature hierarchies derived from shape and kinematic cues. We incorporate
appearance and motion in two ways. First, starting from image region proposals
we select those that are motion salient and thus are more likely to contain the
action. This leads to a significant reduction in the number of regions being
processed and allows for faster computations. Second, we extract
spatio-temporal feature representations to build strong classifiers using
Convolutional Neural Networks. We link our predictions to produce detections
consistent in time, which we call action tubes. We show that our approach
outperforms other techniques in the task of action detection.

本文提出了一种动作检测方法，使用从 2D 图像中物体检测领域的最新进展，利用形状和运动线索建立动作模型，并通过运动显著性选择图像区域提取时空特征建立强分类器，得出了时间一致的动作检测结果。