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

Unsupervised Action Localization Crop in Video Retargeting for 3D ConvNets