Using Shape, Motion and Context for Object Classification in 3D Point Clouds

使用形状、运动和上下文进行 3D 点云中的对象分类

• 批准号: 230795813
• 负责人: Professor Dr.-Ing. Hans-Joachim Wünsche
• 金额: --
• 依托单位: Institut für Technik Autonomer Systeme (LRT-8)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2013
• 资助国家: 德国
• 起止时间: 2012-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/230795813?language=en
• 关键词: Using; Shape; Motion; Context; Object

One of the key elements of autonomous driving is the robust and precise detection, tracking and classification of moving objects. In the previous research on this topic, the shape and the movement of an object was used for classification. For this task, object models containing shape and movement information were created based on hand labeled tracks. In this follow-up project, these models should be learned automatically. To separate the objects into different object classes and to improve the prediction of the movement at the same time, the shape, the movement and the context of an object should be taken into account. Humans use the context in a quite natural way. It can be described as a set of implicit rules generated from experience. For example, pedestrians move on sidewalks, sidewalks are beside the roadway and cars move on the roadway. In this research, a method should be developed that learns the context of an object automatically without the use of fixed rules. The expected benefit is the robust classification and prediction of moving objects even in difficult situations. Examples of such difficult situations are incomplete shape information if self-occlusion or occlusion from other objects is present and missing movement information if an observed object is not moving. The additional combination of the context with the shape and the movements is expected to improve robust classification and the prediction of moving objects.

自动驾驶的关键要素之一是对移动物体进行稳健而精确的检测、跟踪和分类。在以前的研究中，对这一主题，形状和运动的一个对象被用于分类。对于这项任务，对象模型包含形状和运动信息的基础上创建的手标记的轨道。在这个后续项目中，这些模型应该自动学习。为了将目标划分为不同的目标类，同时提高对目标运动的预测能力，需要综合考虑目标的形状、运动和上下文。人类以一种非常自然的方式使用上下文。它可以被描述为一组从经验中产生的隐含规则。例如，行人在人行道上移动，人行道在车行道旁边，而汽车在车行道上移动。在这项研究中，应该开发一种方法，自动学习对象的上下文，而不使用固定的规则。预期的好处是即使在困难的情况下也能对移动物体进行稳健的分类和预测。这种困难情况的示例是如果存在自遮挡或来自其他对象的遮挡则不完整的形状信息，以及如果观察到的对象不移动则丢失移动信息。上下文与形状和运动的附加组合有望提高对运动对象的鲁棒分类和预测。