Learning to Perceive Objects for Autonomous Navigation
Autonomous Robots
IEEE Intelligent Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
Robust Real-Time Face Detection
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
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
MonoSLAM: Real-Time Single Camera SLAM
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection and classification of road signs in natural environments
Neural Computing and Applications
Putting Objects in Perspective
International Journal of Computer Vision
A mapping and localization framework for scalable appearance-based navigation
Computer Vision and Image Understanding
Robust Multiperson Tracking from a Mobile Platform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Monocular Pedestrian Detection: Survey and Experiments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Traffic sign recognition using evolutionary adaboost detection and forest-ECOC classification
IEEE Transactions on Intelligent Transportation Systems
Missing data problems in machine learning
Missing data problems in machine learning
In-vehicle camera traffic sign detection and recognition
Machine Vision and Applications
Using fourier descriptors and spatial models for traffic sign recognition
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Enhancing the point feature tracker by adaptive modelling of the feature support
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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This paper addresses detection, tracking and recognition of traffic signs in video. Previous research has shown that very good detection recalls can be obtained by state-of-the-art detection algorithms. Unfortunately, satisfactory precision and localization accuracy are more difficultly achieved. We follow the intuitive notion that it should be easier to accurately detect an object from an image sequence than from a single image. We propose a novel two-stage technique which achieves improved detection results by applying temporal and spatial constraints to the occurrences of traffic signs in video. The first stage produces well-aligned temporally consistent detection tracks by managing many competing track hypotheses at once. The second stage improves the precision by filtering the detection tracks by a learned discriminative model. The two stages have been evaluated in extensive experiments performed on videos acquired from a moving vehicle. The obtained experimental results clearly confirm the advantages of the proposed technique.