From Handcrafted to Deep Features for Pedestrian Detection : A Survey

Linköpings universitet, Datorseende Linköpings universitet, Tekniska fakulteten Tianjin University, China Tianjin University, China Tianjin University, China School of Computing Sciences, University of East Anglia, UK New York 2022

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.
Funding agencies:10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61906130, 61632018), National Key Research and Development Program of China (Grant Number: 2018AAA0102800)

Feature extraction; Proposals; Cameras; Deep learning; Task analysis; Object detection; Support vector machines; Pedestrian detection; handcrafted features based methods; deep features based methods; multi-spectral pedestrian detection; Computer Vision and Robotics (Autonomous Systems); Datorseende och robotik (autonoma system); Article in journal; info:eu-repo/semantics/article; text

10.1109.TPAMI.2021.3076733

Open access content. Open access content
info:eu-repo/semantics/restrictedAccess

English

UPE oai:DiVA.org:liu-179899
doi:10.1109/TPAMI.2021.3076733
PMID 33929956
ISI:000836666600033
Scopus 2-s2.0-85105102179
1280626556

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