Citation

Dollar P, Wojek C, Schiele B, Perona P. IEEE Trans. Pattern Anal. Mach. Intell. 2012; 34(4): 743-761.

Affiliation

Caltech, Pasadena, California, USA.

Copyright

(Copyright © 2012, Institute of Electrical and Electronics Engineers, Publisher IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TPAMI.2011.155

PMID

21808091

Abstract

Pedestrian detection is a key problem in computer vision, with several applications that have the potential to positively impact quality of life. In recent years, the number of approaches to detecting pedestrians in monocular images has grown steadily. However, multiple datasets and widely varying evaluation protocols are used, making direct comparisons difficult. To address these shortcomings, we perform an extensive evaluation of the state of the art in a unified framework. We make three primary contributions: (1) we put together a large, well-annotated and realistic monocular pedestrian detection dataset and study the statistics of the size, position and occlusion patterns of pedestrians in urban scenes, (2) we propose a refined per-frame evaluation methodology that allows us to carry out probing and informative comparisons, including measuring performance in relation to scale and occlusion, and (3) we evaluate the performance of sixteen pre-trained state-of-the-art detectors across six datasets. Our study allows us to assess the state of the art and provides a framework for gauging future efforts. By determining the key components of successful detectors and common conditions under which existing methods fail, we help identify open problems and future research directions in pedestrian detection.

Language: en