Pedestrian detection is an important but challenging problem in computer vision, especially in human-centric tasks. Over the past decade, significant improvement has been witnessed with the help of handcrafted features and deep features. Here we present a comprehensive survey on recent advances in pedestrian detection. First, we provide a detailed review of single-spectral pedestrian detection that includes handcrafted features based methods and deep features based approaches. For handcrafted features based methods, we present an extensive review of approaches and find that handcrafted features with large freedom degrees in shape and space have better performance. In the case of deep features based approaches, we split them into pure CNN based methods and those employing both handcrafted and CNN based features. We give the statistical analysis and tendency of these methods, where feature enhanced, part-aware, and post-processing methods have attracted main attention. In addition to single-spectral pedestrian detection, we also review multi-spectral pedestrian detection, which provides more robust features for illumination variance. Furthermore, we introduce some related datasets and evaluation metrics, and a deep experimental analysis. We conclude this survey by emphasizing open problems that need to be addressed and highlighting various future directions. Researchers can track an up-to-date list at https://github.com/JialeCao001/PedSurvey.

中文翻译：

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从手工制作到行人检测的深层特征：一项调查


行人检测是计算机视觉中一个重要但具有挑战性的问题，特别是在以人为中心的任务中。在过去的十年中，在手工特征和深度特征的帮助下，已经取得了显着的进步。在这里，我们对行人检测的最新进展进行了全面的调查。首先，我们详细回顾了单光谱行人检测，包括基于手工特征的方法和基于深度特征的方法。对于基于手工制作特征的方法，我们对方法进行了广泛的回顾，发现形状和空间自由度大的手工制作特征具有更好的性能。对于基于深度特征的方法，我们将它们分为纯基于 CNN 的方法和同时采用手工制作和基于 CNN 特征的方法。我们给出了这些方法的统计分析和趋势，其中特征增强、零件感知和后处理方法引起了主要关注。除了单光谱行人检测之外，我们还回顾了多光谱行人检测，它为照明方差提供了更稳健的特征。此外，我们介绍了一些相关的数据集和评估指标，以及深入的实验分析。我们通过强调需要解决的开放性问题并强调各种未来方向来结束本次调查。研究人员可以在 https://github.com/JialeCao001/PedSurvey 上跟踪最新列表。