A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
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
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Detecting Pedestrians Using Patterns of Motion and Appearance
International Journal of Computer Vision
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
An Experimental Study on Pedestrian Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-cue Pedestrian Detection and Tracking from a Moving Vehicle
International Journal of Computer Vision
Pedestrian detection by means of far-infrared stereo vision
Computer Vision and Image Understanding
Multiview pedestrian detection based on vector boosting
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
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
Stereo- and neural network-based pedestrian detection
IEEE Transactions on Intelligent Transportation Systems
Pedestrian detection and tracking with night vision
IEEE Transactions on Intelligent Transportation Systems
Combination of Feature Extraction Methods for SVM Pedestrian Detection
IEEE Transactions on Intelligent Transportation Systems
Pedestrian Protection Systems: Issues, Survey, and Challenges
IEEE Transactions on Intelligent Transportation Systems
A Low-Cost Pedestrian-Detection System With a Single Optical Camera
IEEE Transactions on Intelligent Transportation Systems
Fast Human Detection Using a Novel Boosted Cascading Structure With Meta Stages
IEEE Transactions on Image Processing
A channel awareness vehicle detector
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Performance analysis of pedestrian detection at night time with different classifiers
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
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Pedestrian detection is one of the most important components in driver-assistance systems. In this paper, we propose a monocular vision system for real-time pedestrian detection and tracking during nighttime driving with a near-infrared (NIR) camera. Three modules (region-of-interest (ROI) generation, object classification, and tracking) are integrated in a cascade, and each utilizes complementary visual features to distinguish the objects from the cluttered background in the range of 20-80 m. Based on the common fact that the objects appear brighter than the nearby background in nighttime NIR images, efficient ROI generation is done based on the dual-threshold segmentation algorithm. As there is large intraclass variability in the pedestrian class, a tree-structured, two-stage detector is proposed to tackle the problem through training separate classifiers on disjoint subsets of different image sizes and arranging the classifiers based on Haar-like and histogram-of-oriented-gradients (HOG) features in a coarse-to-fine manner. To suppress the false alarms and fill the detection gaps, template-matching-based tracking is adopted, and multiframe validation is used to obtain the final results. Results from extensive tests on both urban and suburban videos indicate that the algorithm can produce a detection rate of more than 90% at the cost of about 10 false alarms/h and perform as fast as the frame rate (30 frames/s) on a Pentium IV 3.0-GHz personal computer, which also demonstrates that the proposed system is feasible for practical applications and enjoys the advantage of low implementation cost.