  Untrimmed videos on social media or those captured by robots and surveillance
cameras are of varied aspect ratios. However, 3D CNNs usually require as input
a square-shaped video, whose spatial dimension is smaller than the original.
Random- or center-cropping may leave out the video's subject altogether. To
address this, we propose an unsupervised video cropping approach by shaping
this as a retargeting and video-to-video synthesis problem. The synthesized
video maintains a 1:1 aspect ratio, is smaller in size and is targeted at
video-subject(s) throughout the entire duration. First, action localization is
performed on each frame by identifying patches with homogeneous motion
patterns. Thus, a single salient patch is pinpointed per frame. But to avoid
viewpoint jitters and flickering, any inter-frame scale or position changes
among the patches should be performed gradually over time. This issue is
addressed with a polyBezier fitting in 3D space that passes through some chosen
pivot timestamps and whose shape is influenced by the in-between control
timestamps. To corroborate the effectiveness of the proposed method, we
evaluate the video classification task by comparing our dynamic cropping
technique with random cropping on three benchmark datasets, viz. UCF-101,
HMDB-51 and ActivityNet v1.3. The clip and top-1 accuracy for video
classification after our cropping, outperform 3D CNN performances for
same-sized random-crop inputs, also surpassing some larger random-crop sizes.
