Action Recognition by 3D Convolutional Network

Brezovsky, Matus; Sopiak, Dominik; Oravec, Milos

doi:10.23919/elmar.2018.8534657

Cited by 1 publication

(1 citation statement)

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“…Current existing convolutional neural networks (CNNs) for action recognition can be categorized by the type of convolution kernel, i.e., three-dimensional (3D) and two-dimensional (2D) CNN. Several researchers utilized 3D CNNs to learn both spatial and temporal information simultaneously [ 1 , 2 , 3 ]. While this approach works very well for video action recognition tasks, the usage of a CNN-based 3D kernel certainly introduces more parameters compared with the 2D kernel type; hence, the computation cost increases.…”

Section: Introductionmentioning

confidence: 99%

Video Action Recognition Using Motion and Multi-View Excitation with Temporal Aggregation

Joefrie

Aono

2022

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Spatiotemporal and motion feature representations are the key to video action recognition. Typical previous approaches are to utilize 3D CNNs to cope with both spatial and temporal features, but they suffer from huge computations. Other approaches are to utilize (1+2)D CNNs to learn spatial and temporal features in an efficient way, but they neglect the importance of motion representations. To overcome problems with previous approaches, we propose a novel block which makes it possible to alleviate the aforementioned problems, since our block can capture spatial and temporal features more faithfully and efficiently learn motion features. This proposed block includes Motion Excitation (ME), Multi-view Excitation (MvE), and Densely Connected Temporal Aggregation (DCTA). The purpose of ME is to encode feature-level frame differences; MvE is designed to enrich spatiotemporal features with multiple view representations adaptively; and DCTA is to model long-range temporal dependencies. We inject the proposed building block, which we refer to as the META block (or simply “META”), into 2D ResNet-50. Through extensive experiments, we demonstrate that our proposed method architecture outperforms previous CNN-based methods in terms of “Val Top-1 %” measure with Something-Something v1 and Jester datasets, while the META yielded competitive results with the Moment-in-Time Mini dataset.

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Section: Introductionmentioning

confidence: 99%