论文标题
全景人类活动识别
Panoramic Human Activity Recognition
论文作者
论文摘要
为了对拥挤的场景获得更全面的活动理解，在本文中，我们提出了一个新的全景人类活动识别问题（PAR），该问题旨在同时实现个人行动，社会群体活动和全球活动认识。在现实世界中，这是一个充满挑战而实用的问题。对于这个问题，我们开发了一个新颖的层次图神经网络，以逐步代表和建模人类的人类活动和共同的社会关系。我们进一步建立一个基准来评估所提出的方法和其他现有相关方法。实验结果验证了提出的PAR问题的合理性，我们方法的有效性以及基准的实用性。我们将向公众发布源代码和基准，以促进有关此问题的研究。
To obtain a more comprehensive activity understanding for a crowded scene, in this paper, we propose a new problem of panoramic human activity recognition (PAR), which aims to simultaneous achieve the individual action, social group activity, and global activity recognition. This is a challenging yet practical problem in real-world applications. For this problem, we develop a novel hierarchical graph neural network to progressively represent and model the multi-granularity human activities and mutual social relations for a crowd of people. We further build a benchmark to evaluate the proposed method and other existing related methods. Experimental results verify the rationality of the proposed PAR problem, the effectiveness of our method and the usefulness of the benchmark. We will release the source code and benchmark to the public for promoting the study on this problem.
