Corpus ID: 16207205

Automatic Traffic Scene Analysis Using Supervised Machine Learning Algorithms - Backpropagation Neural Networks and Support Vector Machines

  title={Automatic Traffic Scene Analysis Using Supervised Machine Learning Algorithms - Backpropagation Neural Networks and Support Vector Machines},
  author={Heejong Suh and Daehyon Kim and Changsoo Jang},
Automatic traffic scene analysis which has been used for real-time on-road vehicle detection system is essential to many areas of ITS (Intelligent Transport Systems). In order to improve the detection time and accuracy of detection performance, various image processing techniques have been used for real-time vehicle detection. Moreover, Neural Networks have been increasingly and successfully applied to many problems for ITS research topics. Support Vector Machines (SVMs) are currently another… Expand
A Training Method of Convolution Neural Network for Illumination Robust Pedestrian Detection
  • Junmo Jeong
  • Computer Science
  • Int. J. Embed. Real Time Commun. Syst.
  • 2019
A new training method of convolution neural networks for pedestrian detection under the illumination of robust environments of ADAS (Advanced Driver Assistance System) by using the Convolution Neural Network (CNN). Expand
Traditional house recognition
Experimental results show that edge features with ANN achieve good recognition result, and two comparative studies have been conducted, between edge and texture features and between ANN and SVM classifiers to determine which combination will produce better recognition performance. Expand
Smartphone Sensor Value Pattern Analysis with Neural Network
This paper outlines experiments done to show which smartphone sensors can be used in fingerprint indoor positioning, one of the most popular methods of indoor positioning. Expand


Experimental results show that SVMs can provide higher performance in terms of prediction performance than any other models, including Backpropagation. Expand
Pre-processing of inputs to a neural network model for better performance in traffic scene analysis
Artificial neural networks, which hold considerable potential for recognising and classifying spatial and temporal patterns, have been used as an efficient method for automatic traffic surveillance,Expand
Standard and Advanced Backpropagation Models for Image Processing Application in Traffic Engineering
  • Daehyon Kim
  • Computer Science
  • J. Intell. Transp. Syst.
  • 2002
The standard Backpropagation and three enhanced Back Propagation models, BackPropagation with Momentum, Quickprop, and Backpropagsation withMomentum & Prime-offset (BPMP), have been studied to compare their performance in terms of computing cost and predictive accuracy. Expand
Training support vector machines: an application to face detection
  • E. Osuna, R. Freund, F. Girosi
  • Mathematics, Computer Science
  • Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition
  • 1997
A decomposition algorithm that guarantees global optimality, and can be used to train SVM's over very large data sets is presented, and the feasibility of the approach on a face detection problem that involves a data set of 50,000 data points is demonstrated. Expand
Support-Vector Networks
High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition. Expand
An introduction to computing with neural nets
This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components. Expand
Prediction performance of support vector machines on input vector normalization methods
The results showed that the normalization methods could affect the prediction performance of support vector machines and could be useful for determining a proper normalization method to achieve the best performance in SVMs. Expand
Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery
A framework that optimises the occupancy analysis over long periods by including information on the transition in occupancy, when people enter or leave the monitored area, is proposed. Expand
An empirical study of learning speed in back-propagation networks
A new learning algorithm is developed that is faster than standard backprop by an order of magnitude or more and that appears to scale up very well as the problem size increases. Expand
Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models
Two view-based object recognition algorithms are compared: (1) a heuristic algorithm based on oriented filters, and (2) a support vector learning machine trained on low-resolution images of theExpand