Video Analysis 入门

最新推荐文章于 2025-04-25 14:05:13 发布

Wayne2019

最新推荐文章于 2025-04-25 14:05:13 发布

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分类专栏：深度学习与机器学习文章标签：视频分析视频动作识别人类行为识别深度学习计算机视觉

本文链接：https://blog.youkuaiyun.com/Wayne2019/article/details/78908143

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这篇博客总结了视频分析领域的主要任务，包括Activity Net的五个任务：未修剪视频分类、修剪动作识别、时间动作提案、动作定位和视频事件密集描述。还介绍了常用的数据集如KTH、UCF101，以及视频理解任务如动作分类和时序检测。文章探讨了时间维度的挑战，提出了two stream CNN、C3D等技术，并提到了基于图卷积网络的ST-GCN方法。

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主要基于网上一些质量比较高的分享进行总结，以对视频分析领域的常见问题和方法有一个大致的理解。

Activity Net 2017的五个task：
Task 1: Untrimmed Video Classification (ActivityNet)
videos can contain more than one activity, and typically large time lapses of the video are not related with any activity of interest.

Task 2: Trimmed Action Recognition (Kinetics) [New]
videos contain a single activity, and all the clips have a standard duration of ten seconds.

Task 3: Temporal Action Proposals (ActivityNet) [New]
The goal is to produce a set of candidate temporal segments that are likely to contain a human action.

Task 4: Temporal Action Localization (ActivityNet)
This task is intended to evaluate the ability of algorithms to temporally localize activities in untrimmed video sequences. Here, videos can contain more than one activity instance, and mutiple activity categories can appear in the video.

Task 5: Dense-Captioning Events in Videos (Activity