title: Pedestrian detection: an evaluation of the state of the art.
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 data sets 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 data set 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 pretrained state-of-the-art detectors across six data sets. Our study allows us to assess the state of the art and provides a framework for gauging future efforts. Our experiments show that despite significant progress, performance still has much room for improvement. In particular, detection is disappointing at low resolutions and for partially occluded pedestrians.
authors.affiliation: Department of Electrical Engineering, California Institute of Technology, MC 136-93, 1200 E. California Blvd., Pasadena, CA 91125, USA. pdollar@caltech.edu
authors.forename: Piotr
authors.initials: P
authors.lastname: Dollár
authors.forename: Christian
authors.initials: C
authors.lastname: Wojek
authors.forename: Bernt
authors.initials: B
authors.lastname: Schiele
authors.forename: Pietro
authors.initials: P
authors.lastname: Perona
mesh_terms: D001330:Electronic Data Processing; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D012680:Sensitivity and Specificity