用运动历史图提升视频多类动作识别准确率
Multi class activity classification in videos using Motion History Image generation
- 基于运动历史图提取视频时序特征，增强动作表征
- 在单个视频中实现六类动作的准确分类
- 适合实时安全监控与游戏交互场景
人体动作识别在安防到娱乐系统等多个领域备受关注。实时追踪运动并识别动作对关键安防系统至关重要，在娱乐领域尤其是游戏中，对动作和手势的即时响应是系统成功的关键。本文表明，运动历史图（MHI）是一种成熟的框架，能够以多维细节捕捉时序和动作信息，适用于多种应用场景，包括分类任务。我们利用MHI生成样本数据用于训练分类器，并在单个包含六类不同动作的视频中验证了其有效性。通过分析分类器性能，我们识别出MHI在某些情况下难以生成合适活动图像的问题，并讨论了改进机制与未来研究方向。
原文摘要 · Abstract (English)
Human action recognition has been a topic of interest across multiple fields ranging from security to entertainment systems. Tracking the motion and identifying the action being performed on a real time basis is necessary for critical security systems. In entertainment, especially gaming, the need for immediate responses for actions and gestures are paramount for the success of that system. We show that Motion History image has been a well established framework to capture the temporal and activity information in multi dimensional detail enabling various usecases including classification. We utilize MHI to produce sample data to train a classifier and demonstrate its effectiveness for action classification across six different activities in a single multi-action video. We analyze the classifier performance and identify usecases where MHI struggles to generate the appropriate activity image and discuss mechanisms and future work to overcome those limitations.
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