Traffic Sign Recognition using CNN and Keras

Application of the German Traffic Sign Recognition Benchmark on the VGG16 network using transfer learning and bottleneck features in Keras

Traffic Sign Recognition Using Discriminative Local Features

Real-time road sign recognition has been of great interest for many years. This problem is often addressed in a two-stage procedure involving detection and classification. In this paper a novel approach to sign classification is proposed. In many previous studies focus was put on deriving a possibly most discriminative set of features from a large amount of training data using global selection ...

Deep Sign: Hybrid CNN-HMM for Continuous Sign Language Recognition

This paper introduces the end-to-end embedding of a CNN into a HMM, while interpreting the outputs of the CNN in a Bayesian fashion. The hybrid CNN-HMM combines strong discriminative abilities of CNNs with sequence modeling capabilities of HMMs. Most current approaches in the field of gesture and sign language recognition disregard the necessity of dealing with sequence data both for training a...

Mobile Traffic Sign Recognition

Traffic sign recognition through artificial intelligence tools is an attractive topic in the computer vision community for its clear applications in the automotive industry. This problem is a subset of the larger problem of improving safe driving through intelligent technologies that can recognize objects on the road and help drivers avoid hazardous situations. In this work we evaluated a numbe...

Traffic Sign Recognition

Traffic sign recognition is a difficult task if aim is at detecting and recognizing signs in images captured from unfavorable environments. Complex background, weather, shadow, and other lighting-related problems may make it difficult to detect and recognize signs in the rural as well as the urban areas. Two major problems exist in the whole detection process. Road signs are frequently occluded...