Introduction:

Action recognition is a challenging time series classification task that has received much attention in the recent past due to its importance in critical applications, such as surveillance, visual behavior study, topic discovery, security, and content retrieval.

Objectives:

The main objective of the research is to develop a robust and high-performance human action recognition techniques. A combination of local and holistic feature extraction methods used through analyzing the most effective features to extract to reach the objective, followed by using simple and high-performance machine learning algorithms.

Methods:

This paper presents three robust action recognition techniques based on a series of image analysis methods to detect activities in different scenes. The general scheme architecture consists of shot boundary detection, shot frame rate re-sampling, and compact feature vector extraction. This process is achieved by emphasizing variations and extracting strong patterns in feature vectors before classification.

Results:

The proposed schemes are tested on datasets with cluttered backgrounds, low- or high-resolution videos, different viewpoints, and different camera motion conditions, namely, the Hollywood-2, KTH, UCF11 (YouTube actions), and Weizmann datasets. The proposed schemes resulted in highly accurate video analysis results compared to those of other works based on four widely used datasets. The First, Second, and Third Schemes provides recognition accuracies of 57.8%, 73.6%, and 52.0% on Hollywood2, 94.5%, 97.0%, and 59.3% on KTH, 94.5%, 95.6%, and 94.2% on UCF11, and 98.9%, 97.8% and 100% on Weizmann.

Conclusion:

Each of the proposed schemes provides high recognition accuracy compared to other state-of-art methods. Especially, the Second Scheme as it gives excellent comparable results to other benchmarked approaches.

中文翻译：

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用于人类动作识别的鲁棒视频内容分析方案

介绍：

动作识别是一项具有挑战性的时间序列分类任务，由于其在监控、视觉行为研究、主题发现、安全和内容检索等关键应用中的重要性，近年来受到了广泛关注。

目标：

该研究的主要目标是开发一种强大且高性能的人类动作识别技术。结合局部和整体特征提取方法，通过分析提取最有效的特征来达到目标​​，然后使用简单且高性能的机器学习算法。

方法：

本文提出了三种基于一系列图像分析方法的鲁棒动作识别技术来检测不同场景中的活动。总体方案架构由镜头边界检测、镜头帧率重采样和紧凑特征向量提取组成。这个过程是通过在分类之前强调变化并提取特征向量中的强模式来实现的。

结果：

所提出的方案在具有杂乱背景、低或高分辨率视频、不同视点和不同相机运动条件的数据集（即Hollywood-2、KTH、UCF11（YouTube actions）和Weizmann数据集）上进行了测试。与基于四个广泛使用的数据集的其他作品相比，所提出的方案产生了高度准确的视频分析结果。第一、第二和第三方案在Hollywood2上的识别准确率分别为57.8%、73.6%和52.0%，在KTH上提供94.5%、97.0%和59.3%，在UCF11上提供94.5%、95.6%和94.2%，在UCF11上提供98.9%的识别准确率。魏茨曼的 %、97.8% 和 100%。

结论：

与其他最先进的方法相比，所提出的每个方案都提供了较高的识别精度。特别是第二个方案，因为它与其他基准方法相比提供了出色的可比结果。