This paper describes a system for people indoor localization using omnidirectional cameras and machine learning that significantly reduces the database annotation requirements for the training stage. The most prominent works for people detection are based on machine learning techniques that requires large databases with bounding box annotations (that enclose the people). In this work, a novel multiple classifier system, called Grid of Spatial-Aware Classifiers (GSAC), is proposed, which only requires point-based annotations, allowing to create datasets much faster (and therefore speeding up the system deployment). On the other hand, omnidirectional images have a wider field of view than traditional ones, allowing to monitor a wider area, and thus reducing deployment costs. But in return, they present severe geometric distortions that degrade the performance of state of the art detectors, due to the strong changes in the person appearance caused by the position-dependent distortion. The proposed GSAC satisfactorily addresses this problem by distributing the detection task among all spatial-aware classifiers, so that each classifier has only to deal with a subrange of appearances and distortions. Lastly, a thorough evaluation has been performed on two databases of omnidirectional images: a well-known one and one specifically designed to assess the people detection performance.

本文介绍了一种使用全向摄像机和机器学习进行人员室内定位的系统，该系统可大大减少培训阶段的数据库注释要求。用于人检测的最杰出的作品是基于机器学习技术的，该技术需要带有边界框注释（包围人）的大型数据库。在这项工作中，提出了一种新颖的多重分类器系统，称为空间感知分类器网格（GSAC），它仅需要基于点的注释，从而可以更快地创建数据集（从而加快了系统部署速度）。另一方面，全向图像比传统图像具有更宽的视野，可以监视更大的区域，从而降低了部署成本。但是作为回报，由于位置相关的变形会导致人的外观发生强烈变化，因此它们会呈现出严重的几何变形，从而降低现有检测器的性能。提出的GSAC通过在所有空间感知的分类器之间分配检测任务来令人满意地解决了这个问题，因此每个分类器仅需要处理外观和失真的子范围。最后，对两个全向图像数据库进行了彻底的评估：一个众所周知的数据库和一个专门设计用来评估人员检测性能的数据库。提出的GSAC通过在所有空间感知的分类器之间分配检测任务来令人满意地解决了这个问题，因此每个分类器仅需要处理外观和失真的子范围。最后，对两个全向图像数据库进行了彻底的评估：一个众所周知的数据库和一个专门设计用来评估人员检测性能的数据库。提出的GSAC通过在所有空间感知的分类器之间分配检测任务来令人满意地解决了这个问题，因此每个分类器仅需要处理外观和失真的子范围。最后，对两个全向图像数据库进行了彻底的评估：一个众所周知的数据库和一个专门设计用来评估人员检测性能的数据库。