Monocular Pedestrian Detection: Survey and Experiments

Pedestrian detection is a rapidly evolving area in computer vision with key applications in intelligent vehicles, surveillance, and advanced robotics. The objective of this paper is to provide an overview of the current state of the art from both methodological and experimental perspectives. The fir...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2009-12, Vol.31 (12), p.2179-2195
Main Authors: Enzweiler, M., Gavrila, D.M.
Format: Article
Language:English
Subjects:
Application software
Applied sciences
Artificial intelligence
benchmarking
Cascades
Computer science
control theory
systems
Computer vision
Data processing. List processing. Character string processing
Exact sciences and technology
Image resolution
Intelligent robots
Intelligent vehicles
Mathematical models
Memory organisation. Data processing
Neural networks
Onboard
Pattern recognition. Digital image processing. Computational geometry
Pedestrian detection
Pedestrians
performance analysis
Robot vision systems
Software
State of the art
Studies
Surveillance
survey
Vehicle detection
Vehicle driving
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Summary:Pedestrian detection is a rapidly evolving area in computer vision with key applications in intelligent vehicles, surveillance, and advanced robotics. The objective of this paper is to provide an overview of the current state of the art from both methodological and experimental perspectives. The first part of the paper consists of a survey. We cover the main components of a pedestrian detection system and the underlying models. The second (and larger) part of the paper contains a corresponding experimental study. We consider a diverse set of state-of-the-art systems: wavelet-based AdaBoost cascade, HOG/linSVM, NN/LRF, and combined shape-texture detection. Experiments are performed on an extensive data set captured onboard a vehicle driving through urban environment. The data set includes many thousands of training samples as well as a 27-minute test sequence involving more than 20,000 images with annotated pedestrian locations. We consider a generic evaluation setting and one specific to pedestrian detection onboard a vehicle. Results indicate a clear advantage of HOG/linSVM at higher image resolutions and lower processing speeds, and a superiority of the wavelet-based AdaBoost cascade approach at lower image resolutions and (near) real-time processing speeds. The data set (8.5 GB) is made public for benchmarking purposes.
ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2008.260