Ordered Pooling of Optical Flow Sequences for Action Recognition

By Jue Wang, Anoop Cherian, Fatih Murat Porikli. 2017

47 citations recorded by the source corpus.

Training of Convolutional Neural Networks (CNNs) on long video sequences is computationally expensive due to the substantial memory requirements and the massive number of parameters that deep architectures demand. Early fusion of video frames is thus a standard technique, in which several consecutive frames are first agglomerated into a compact representation, and then fed into the CNN as an input sample. For this purpose, a summarization approach that represents a set of consecutive RGB frames by a single dynamic image to capture pixel dynamics is proposed recently. In this paper, we introduce a novel ordered representation of consecutive optical flow frames as an alternative and argue that this representation captures the action dynamics more efficiently than RGB frames. We provide intuitions on why such a representation is better for action recognition. We validate our claims on standard benchmark datasets and demonstrate that using summaries of flow images lead to significant improvements over RGB frames while achieving accuracy comparable to the stateof-the-art on UCF101 and HMDB datasets.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Video Representation Learning Using Discriminative Pooling (2018, positive): “There have been other recent works that use parameters of machine learning algorithms for the purpose of pooling, such as rank pooling [14], generalized rank pooling [6], dynamic images [2] and dynamic ﬂow [52].”
Generalized Rank Pooling for Activity Recognition (2017, methodology): “A similar observation is also made in [46].”