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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 34; no. 4; pp. 743 - 761
Main Authors: Dollar, P., Wojek, C., Schiele, B., Perona, P.
Format: Journal Article
Language:English
Published: Los Alamitos, CA IEEE 01.04.2012
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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
Performance evaluation
Occlusion
Caltech Pedestrian data set
Computer vision
Statistical analysis
Image processing
data set
Pedestrian
Monocular vision
Low resolution
Pedestrian detection
Object recognition
Annotation
benchmark
Quality of life
evaluation
Object detection
Occultation
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Undefined-1
ObjectType-Feature-3
content type line 23
ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2011.155