TL
TL;DR
The dataset is described, the statistics are described, how it was collected, and some baseline performance figures for neural network architectures trained and tested for human action classification on this dataset are given.
Abstract
We describe the DeepMind Kinetics human action video dataset. The dataset contains 400 human action classes, with at least 400 video clips for each action. Each clip lasts around 10s and is taken from a different YouTube video. The actions are human focussed and cover a broad range of classes including human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands. We describe the statistics of the dataset, how it was collected, and give some baseline performance figures for neural network architectures trained and tested for human action classification on this dataset. We also carry out a preliminary analysis of whether imbalance in the dataset leads to bias in the classifiers.
References28 items
1
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
2
Semantics derived automatically from language corpora contain human-like biases
3
Convolutional Two-Stream Network Fusion for Video Action Recognition
4
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
5