Human action recognition still exists many challenging problems such as different viewpoints, occlusion, lighting conditions, human body size and the speed of action execution, although it has been widely used in different areas. To tackle these challenges, the Kinect depth sensor has been developed to record real time depth sequences, which are insensitive to the color of human clothes and illumination conditions. Many methods on recognizing human action have been reported in the literature such as HON4D, HOPC, RBD and HDG, which use the 4D surface normals, pointclouds, skeleton-based model and depth gradients respectively to capture discriminative information from depth videos or skeleton data. In this research project, the performance of four aforementioned algorithms will be analyzed and evaluated using five benchmark datasets, which cover challenging issues such as noise, change of viewpoints, background clutters and occlusions. We also implemented and improved the HDG algorithm, and applied it in cross-view action recognition using the UWA3D Multiview Activity dataset. Moreover, we used different combinations of individual feature vectors in HDG for performance evaluation. The experimental results show that our improvement of HDG outperforms other three state-of-the-art algorithms for cross-view action recognition.

翻译：人体动作识别尽管在不同领域已得到广泛应用，但仍存在诸多挑战性问题，如视角差异、遮挡、光照条件、人体尺寸及动作执行速度等。为应对这些挑战，Kinect深度传感器被开发用于记录实时深度序列，其对人体衣物颜色和光照条件不敏感。文献中已报道多种人体动作识别方法，例如HON4D、HOPC、RBD及HDG算法，这些方法分别利用4D表面法线、点云、基于骨骼的模型及深度梯度，从深度视频或骨骼数据中提取判别性信息。本研究项目将使用五个基准数据集对上述四种算法的性能进行分析与评估，这些数据集涵盖了噪声、视角变化、背景杂乱及遮挡等挑战性问题。我们还实现并改进了HDG算法，并将其应用于基于UWA3D多视角活动数据集的跨视角动作识别。此外，我们采用HDG中不同个体特征向量的组合进行性能评估。实验结果表明，我们改进的HDG算法在跨视角动作识别中优于其他三种最先进算法。