The Strength of Weak Learnability
Machine Learning
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
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
Autonomous Driving Goes Downtown
IEEE Intelligent Systems
Pedestrian Detection from a Moving Vehicle
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Pedestrian Detection Using Wavelet Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Improving SVM accuracy by training on auxiliary data sources
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Logistic regression with an auxiliary data source
ICML '05 Proceedings of the 22nd international conference on Machine learning
Active Learning Based Pedestrian Detection in Real Scenes
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multisensor-based human detection and tracking for mobile service robots
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning an intrinsic-variable preserving manifold for dynamic visual tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Survey of Pedestrian Detection for Advanced Driver Assistance Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Pedestrian Detection Using Covariance Descriptor and On-line Learning
TAAI '11 Proceedings of the 2011 International Conference on Technologies and Applications of Artificial Intelligence
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
Pedestrian detection and tracking with night vision
IEEE Transactions on Intelligent Transportation Systems
A Low-Cost Pedestrian-Detection System With a Single Optical Camera
IEEE Transactions on Intelligent Transportation Systems
An Efficient Tree Classifier Ensemble-Based Approach for Pedestrian Detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rapid pedestrian detection in unseen scenes
Neurocomputing
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Adapting trained detectors to unseen scenes is a critical problem in pedestrian detection. The performance of trained detector may drop quickly when scenes vary significantly. Retraining a detector with labeled samples from the new scenes may improve its performance. However, it is difficult to obtain enough labeled samples in real applications. In this paper, a novel bag of visual words based method is proposed to detect pedestrians in unseen scenes by dynamically updating the key words. The proposed method achieves its adaptability by using three strategies covering key word selection, detector invariance, and codebook update: (1) In order to select typical words representing pedestrians, a low dimensional model of visual words is built to describe their distribution and select key words using manifold learning. (2) Matching confidence vector (MCV), a novel visual words measurement is proposed, which aims to generate a uniform input vector for the fixed detector applied to different pedestrian codebooks. (3) When detecting pedestrians under changing road conditions, the key word set will be dynamically adjusted according to the matching frequency of each word to adapt the detector to the new scenes. By employing the above strategies, the proposed method is able to detect pedestrians in different scenes without retraining the detector. Experiments in different scenes showed that our proposed method can achieve better adaptability to various scenes and get better performance than other existing methods in unseen scenes.