Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
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
A Bayesian, Exemplar-Based Approach to Hierarchical Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
A Performance Evaluation of Single and Multi-feature People Detection
Proceedings of the 30th DAGM symposium on Pattern Recognition
Combination of Feature Extraction Methods for SVM Pedestrian Detection
IEEE Transactions on Intelligent Transportation Systems
Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features
IEEE Transactions on Circuits and Systems for Video Technology
Halftone Image Classification Using LMS Algorithm and Naive Bayes
IEEE Transactions on Image Processing
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The pedestrian detection is a popular research field in recent years, yet the low-resolution issue is rarely discussed for yielding detection accuracy for drivers. In this study, a hierarchical pedestrian detection system is proposed to cope with this issue. In which, two independent features, orientation and magnitude, are adopted as descriptors for pedestrians. Moreover, the proposed probability-based pedestrian mask pre-filtering (PPMPF) is utilized to initially filter out non-pedestrian regions meanwhile retaining most of the real pedestrians. In experimental results, the use of the two proposed features can provide superior performance than the former well-known histogram of oriented gradient (HOG; high accuracy) and the edgelet (high processing efficiency) simultaneously without carrying their lacks. Moreover, the PPMPF can also boost the processing efficiency by a factor of around 2.82 in contrast to the system without this pre-filtering strategy. Thus, the proposed method can be a very competitive candidate for intelligent surveillance applications.