Pedestrian Detection: An Evaluation of the State of the Art

TLDR

Pedestrian detection is a critical computer vision task with many applications, yet the proliferation of datasets and inconsistent evaluation protocols hampers direct comparison of methods. This study evaluates the state of the art by assembling a large, realistic pedestrian dataset, proposing a refined per‑frame evaluation protocol, and benchmarking sixteen pretrained detectors across six datasets. The authors built a unified evaluation framework that includes a comprehensive dataset, a per‑frame metric sensitive to scale and occlusion, and a systematic comparison of sixteen detectors on six datasets. The experiments reveal that current detectors still underperform, particularly on low‑resolution images and partially occluded pedestrians.

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.