MPSC_MV2

Deep video representation learning has recently attained state-of-the-art performance in video action recognition. However, when used with video clips from varied perspectives, the performance of these models degrades significantly. Existing VAR models frequently simultaneously contain both view information and action attributes, making it difficult to learn a view-invariant representation. Therefore, to study the attribute of multiview representation, we collected a large-scale time synchronous multiview video dataset from 10 subjects in both indoor and outdoor settings performing 10 different actions with three horizontal and vertical viewpoints using a smartphone, an action camera, and a drone camera. We provide the multiview video dataset with various meta-data information to facilitate further research for robust VAR systems.深度视频表征学习（Deep Video Representation Learning）目前在视频动作识别领域已取得当前最优性能。然而，当此类模型应用于不同视角的视频片段时，其性能会出现显著下滑。现有的视频动作识别（Video Action Recognition, VAR）模型通常同时包含视角信息与动作属性，这使得学习视角不变的表征变得极为困难。为此，为了研究多视角表征的特性，我们采集了一个大规模时序同步多视角视频数据集：招募10名受试者，在室内与室外两种场景下完成10种不同动作，并通过智能手机、运动相机及无人机三种设备，从三个水平与垂直视角进行录制，所有视角的视频均实现时序同步。本数据集附带丰富的元数据信息，可为鲁棒VAR系统的相关研究提供支撑，助力该领域的进一步探索。

搜集汇总

背景与挑战

背景概述

MPSC_MV2是一个大规模时间同步多视角视频数据集，旨在解决视频动作识别模型在不同视角下性能下降的问题。它包含10名受试者在室内外执行10个动作，从多个水平和垂直视角采集，并提供了丰富的元数据，以促进鲁棒视频动作识别系统的研究。