Pedestrian Detection: An Evaluation of the State of the Art

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...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2012-04, Vol.34 (4), p.743-761
Main Authors: Dollar, P., Wojek, C., Schiele, B., Perona, P.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Automatic Data Processing
benchmark
Caltech Pedestrian data set
Cameras
Computer science
control theory
systems
Detectors
evaluation
Exact sciences and technology
Heating
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Labeling
object detection
Pattern Recognition, Automated - methods
Pattern recognition. Digital image processing. Computational geometry
Pedestrian detection
Pixel
Sensitivity and Specificity
Studies
Testing
Training
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Summary: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.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2011.155