A Multi-viewpoint Outdoor Dataset for Human Action Recognition

TL;DR

This paper introduces a new multi-viewpoint outdoor human action recognition dataset from YouTube and drone footage, addressing the lack of multi-view datasets and enabling improved action recognition research.

Contribution

It provides a large, multi-viewpoint dataset with 20 action classes, and evaluates baseline recognition performance using a two-stream CNN with kernelized rank pooling.

Findings

Baseline accuracy of 74.0% on the dataset

Dataset includes 2324 videos and over 500,000 frames

Supports research in action recognition, surveillance, and situational awareness

Abstract

Advancements in deep neural networks have contributed to near perfect results for many computer vision problems such as object recognition, face recognition and pose estimation. However, human action recognition is still far from human-level performance. Owing to the articulated nature of the human body, it is challenging to detect an action from multiple viewpoints, particularly from an aerial viewpoint. This is further compounded by a scarcity of datasets that cover multiple viewpoints of actions. To fill this gap and enable research in wider application areas, we present a multi-viewpoint outdoor action recognition dataset collected from YouTube and our own drone. The dataset consists of 20 dynamic human action classes, 2324 video clips and 503086 frames. All videos are cropped and resized to 720x720 without distorting the original aspect ratio of the human subjects in videos. This dataset should be useful to many research areas including action recognition, surveillance and situational awareness. We evaluated the dataset with a two-stream CNN architecture coupled with a recently proposed temporal pooling scheme called kernelized rank pooling that produces nonlinear feature subspace representations. The overall baseline action recognition accuracy is 74.0%.