A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Feature Extraction for Traffic Incident Detection Using Wavelet Transform and Linear Discriminant Analysis
To eliminate false alarms, an effective traffic incident detection algorithm must be able to extract incident‐related features from the traffic patterns. A robust feature‐extraction algorithm also helps reduce the dimension of the input space for a neural network model without any significant loss of related traffic information, resulting in a substantial reduction in the network size, the effect of random traffic fluctuations, the number of required training samples, and the computational resources required to train the neural network. This article presents an effective traffic feature‐extraction model using discrete wavelet transform (DWT) and linear discriminant analysis (LDA). The DWT is first applied to raw traffic data, and the finest resolution coefficients representing the random fluctuations of traffic are discarded. Next, LDA is employed to the filtered signal for further feature extraction and reducing the dimensionality of the problem. The results of LDA are used as input to a neural network model for traffic incident detection.
Feature Extraction for Traffic Incident Detection Using Wavelet Transform and Linear Discriminant Analysis
To eliminate false alarms, an effective traffic incident detection algorithm must be able to extract incident‐related features from the traffic patterns. A robust feature‐extraction algorithm also helps reduce the dimension of the input space for a neural network model without any significant loss of related traffic information, resulting in a substantial reduction in the network size, the effect of random traffic fluctuations, the number of required training samples, and the computational resources required to train the neural network. This article presents an effective traffic feature‐extraction model using discrete wavelet transform (DWT) and linear discriminant analysis (LDA). The DWT is first applied to raw traffic data, and the finest resolution coefficients representing the random fluctuations of traffic are discarded. Next, LDA is employed to the filtered signal for further feature extraction and reducing the dimensionality of the problem. The results of LDA are used as input to a neural network model for traffic incident detection.
Feature Extraction for Traffic Incident Detection Using Wavelet Transform and Linear Discriminant Analysis
Samant, A. (author) / Adeli, H. (author)
Computer‐Aided Civil and Infrastructure Engineering ; 15 ; 241-250
2000-07-01
10 pages
Article (Journal)
Electronic Resource
English
Incident Detection Algorithm using Wavelet Energy Representation of Traffic Patterns
| Online Contents | 2002
Feature Extraction using Wavelet Transform for Multi-class Fault Detection of Induction Motor
| Springer Verlag | 2014
Feature Extraction from Acoustic Emission Waveforms using a Discrete Wavelet Transform
| British Library Conference Proceedings | 2008
Semisupervised local discriminant analysis for feature extraction in hyperspectral images
| Online Contents | 2013