  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
compare some representative methods. 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.
