Probabilistic Vehicle Identification Techniques for Semiautomated Transportation Security

Abstract

Intelligent transportation systems can play a significant role in transportation security in addition to their traditional roles in transportation operations and management. A multidetector semiautomated vehicle surveillance framework is presented. The objective of the framework is to assist in the search for a vehicle of interest involved with security threats such as terrorism, abduction, or crime. When a vehicle of interest is wanted, this framework can be applied to reduce surveillance data sets and thus reduce time and labor. This system estimates the a posteriori probabilities that indicate the closeness of the match between a vehicle of interest and any vehicle in the search space. This paper explores the use of multidetector fusion of video and inductive loop data by means of a linear fusion model. This system classifies vehicle pairs into possible correct match or incorrect match classes and transforms the problem into the probabilistic domain by using Bayesian neural networks and probabilistic neural networks (PNNs). The use of Bayesian and PNN classifiers assumes equal losses. With Bayesian estimation and generalized regression neural networks, the a posteriori probability is used as a threshold representing unequal losses. A comparison between the traditional Bayesian approaches and the equivalent neural network methods is presented. The use of different feature combinations, methods to balance training data sets, forward sequential search, and combined and uncombined feature approaches is also investigated. Field arterial data from southern California show that, by retaining only 29% of the search space, the framework produces 92% accuracy, which is a promising result.