A Trainable System for Object Detection
International Journal of Computer Vision - special issue on learning and vision at the center for biological and computational learning, Massachusetts Institute of Technology
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Shape Based Object Classification for Automated Video Surveillance with Feature Selection
ICIT '07 Proceedings of the 10th International Conference on Information Technology
Object detection using image reconstruction with PCA
Image and Vision Computing
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object detection combining recognition and segmentation
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Letters: Laplacian bidirectional PCA for face recognition
Neurocomputing
Nighttime pedestrian detection with a normal camera using SVM classifier
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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The detection of pedestrian has attracted much research in the past decade due to the essential role it plays in intelligent video surveillance and vehicle vision systems. However, the existing algorithms do not meet the requirement of real applications in terms of detection performance. This paper proposes a new robust algorithm for pedestrian detection based on image reconstruction using bidirectional PCA (BDPCA). Unlike PCA, since it is a straightforward image projection technique, BDPCA preserves the shape structure of objects and is computationally effective. Due to these advantages, BDPCA is a promising tool for object detection and recognition. The algorithm was tested on two datasets, INRIA and PennFudanPed. Our experiment proved that using BDPCA with vertical edge images was the most suitable for pedestrian detection. The comparison between BDPCA, PCA, and histogram of oriented gradient (HOG) based methods demonstrates superior accuracy and robustness of the proposed algorithm to the others.