Recognizing human actions, reliably inferring their meaning and being able to potentially exchange mutual social information are core challenges for autonomous systems when they directly share the same space with humans. Intelligent transport systems in particular face this challenge, as interactions with people are often required. The development and testing of technical perception solutions is done mostly on standard vision benchmark datasets for which manual labelling of sensory ground truth has been a tedious but necessary task. Furthermore, rarely occurring human activities are underrepresented in these datasets, leading to algorithms not recognizing such activities. For this purpose, we introduce a modular simulation framework, which offers to train and validate algorithms on various human-centred scenarios. We describe the usage of simulation data to train a state-of-the-art human pose estimation algorithm to recognize unusual human activities in urban areas. Since the recognition of human actions can be an important component of intelligent transport systems, we investigated how simulations can be applied for his purpose. Laboratory experiments show that we can train a recurrent neural network with only simulated data based on motion capture data and 3D avatars, which achieves an almost perfect performance in the classification of those human actions on real data.

中文翻译：

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通过仿真增强数据驱动的人体姿势估计和动作识别算法


当自主系统与人类直接共享同一空间时，识别人类行为、可靠地推断其含义以及能够交换相互的社会信息是其面临的核心挑战。智能交通系统尤其面临这一挑战，因为经常需要与人互动。技术感知解决方案的开发和测试主要是在标准视觉基准数据集上完成的，其中手动标记感官基础事实是一项繁琐但必要的任务。此外，很少发生的人类活动在这些数据集中的代表性不足，导致算法无法识别此类活动。为此，我们引入了一个模块化模拟框架，它可以在各种以人为中心的场景中训练和验证算法。我们描述了如何使用模拟数据来训练最先进的人体姿势估计算法，以识别城市地区不寻常的人类活动。由于对人类行为的识别可能是智能交通系统的重要组成部分，因此我们研究了如何应用模拟来实现其目的。实验室实验表明，我们可以仅使用基于动作捕捉数据和 3D 头像的模拟数据来训练循环神经网络，这在对真实数据上的人类动作进行分类方面实现了近乎完美的性能。