Abstract
In this work, a pedestrian detection method based on adaptive boosting is proposed. The proposed method works on still images. The features utilized in the work are derived from Haar-like templates. An Adaboost classifier is utilized for both feature selection and classification. To show the effectiveness of the proposed algorithm, the system is trained by using Nicta Pedestrian Dataset and tested by using Penn Fudan Pedestrian Dataset. The experimental result shows the proposed method's effectiveness.
Keywords
Pedestrian detection
Computer science
Pedestrian
Boosting (machine learning)
Artificial intelligence
AdaBoost
Haar-like features
Pattern recognition (psychology)
Feature extraction
Feature selection
Classifier (UML)
Object detection
Machine learning
Computer vision
Face detection
Engineering
Facial recognition system
Subject Areas
Video Surveillance and Tracking Methods
·Computer Vision and Pattern Recognition ·Physical Sciences
Advanced Neural Network Applications
·Computer Vision and Pattern Recognition ·Physical Sciences
Anomaly Detection Techniques and Applications
·Artificial Intelligence ·Physical Sciences
Citations by Year
2012: 1
2013: 1
2017: 1
2018: 1
2019: 1
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