Journal ArticleDOI
A novel multiplex cascade classifier for pedestrian detection
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TLDR
A novel multiplex classifier model, which is composed of two multiplex cascades parts: Haar-like cascade classifier and shapelet cascade classifiers, which filters out most of irrelevant image background and detects intensively head-shoulder features.About:
This article is published in Pattern Recognition Letters.The article was published on 2013-10-01. It has received 18 citations till now. The article focuses on the topics: Margin classifier & Quadratic classifier.read more
Citations
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Journal ArticleDOI
Automated Visual Inspection System for Bogie Block Key Under Complex Freight Train Environment
Liu Liu,Fuqiang Zhou,Yuzhu He +2 more
TL;DR: A hierarchical inspection framework containing bearing cap (BC) detection, fault region localization, and BBK classification is proposed, and a BBK classifier based on the GCCM features and a support vector machine is used to process the fault region to identify the missing of BBK.
Journal ArticleDOI
Progressive subspace ensemble learning
TL;DR: The progressive subspace ensemble learning approach (PSEL) which takes into account the data sample space and the feature space at the same time and outperforms a number of state-of-the-art classifier ensemble approaches.
Journal ArticleDOI
Two-Stage Classification Approach for Human Detection in Camera Video in Bulk Ports
TL;DR: The experimental results in Tianjin port show that the two-stage classifier can improve the classification accuracy of human detection obviously.
Journal ArticleDOI
An effective learning strategy for cascaded object detection
TL;DR: This work proposes a learning strategy aimed at maximizing the node classifiers ranking capability rather than their accuracy, and provides an efficient implementation yielding the same time complexity of the original Viola-Jones cascade training.
Journal ArticleDOI
Comparison of 2D image models in segmentation performance for 3D laser point clouds
TL;DR: It is argued that 2D image models greatly reduce the time cost of scene segmentation with a little loss of accuracy and the usage of 2Dimage models is not limited inscene segmentation since robust features can be extracted from 2D picture models to accomplish laser point classification and scene understanding.
References
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Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Proceedings ArticleDOI
Rapid object detection using a boosted cascade of simple features
Paul A. Viola,Michael Jones +1 more
TL;DR: A machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates and the introduction of a new image representation called the "integral image" which allows the features used by the detector to be computed very quickly.
Journal ArticleDOI
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Yoav Freund,Robert E. Schapire +1 more
TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
Journal ArticleDOI
Shape matching and object recognition using shape contexts
TL;DR: This paper presents work on computing shape models that are computationally fast and invariant basic transformations like translation, scaling and rotation, and proposes shape detection using a feature called shape context, which is descriptive of the shape of the object.
Journal ArticleDOI
Pedestrian Detection: An Evaluation of the State of the Art
TL;DR: An extensive evaluation of the state of the art in a unified framework of monocular pedestrian detection using sixteen pretrained state-of-the-art detectors across six data sets and proposes a refined per-frame evaluation methodology.