We investigate video classification via a two-stream convolutional neural network (CNN)
design that directly ingests information extracted from compressed video bitstreams. Our
approach begins with the observation that all modern video codecs divide the input frames
into macroblocks (MBs). We demonstrate that selective access to MB motion vector (MV)
information within compressed video bitstreams can also provide for selective, motion-
adaptive, MB pixel decoding (aka, MB texture decoding). This in turn allows for the …