Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Unified Bias-Variance Decomposition for Zero-One and Squared Loss
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Detection Using GPU-Based Convolutional Neural Networks
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Survey of Pedestrian Detection for Advanced Driver Assistance Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Intelligent Transportation Systems
Real time traffic sign detection using color and shape-based features
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Probabilistic lane estimation for autonomous driving using basis curves
Autonomous Robots
Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation
IEEE Transactions on Intelligent Transportation Systems
Road-Sign Detection and Recognition Based on Support Vector Machines
IEEE Transactions on Intelligent Transportation Systems
Automatic on-the-fly extrinsic camera calibration of onboard vehicular cameras
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Traffic sign detection is a useful application for driving assistance systems, and it is necessary to accurately detect traffic signs before they can be identified. Sometimes, however, it is difficult to detect traffic sign, which may be obscured by other objects or affected by illumination or lightning reflections. Most previous work on this topic has been based on region of interest analysis using the color information of traffic signs. Although this provides a simple way to segment signs, this approach is weak when a sign is affected by illumination or its own color information is distorted. To overcome this, this paper introduces a robust traffic detection framework for cluttered scenes or complex city views that does not use color information. Moreover, the proposed method can detect traffic sign in the night. We establish an edge-adaptive Gabor function, which is derived from human visual perception. It is an enhanced version of the original Gabor filter, and filters out unnecessary information to provide robust recognition. It decomposes the directional information of objects and reflects specific shapes of traffic signs. Once the extracted feature is obtained, a support vector machine detects the traffic sign. Applying scale-space theory, it is possible to resolve the scaling problem of the objects that we want to find. Our system shows robust performance in traffic sign detection, and experiments on real-world scenes confirmed its properties.