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Inductive classifying artificial network for vehicle type categorization
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DOI：10.1111/1467-8667.00307.png)
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Abstract
En 中文
As transportation surveillance technology, continues to advance, the measurement of more complete traffic information is becoming increasingly feasible. ICAN stands for Inductive Classifying Artificial Network and is used to conveniently describe a self-organizing feature map (SOFM) for vehicle type categorization using inductive signatures as input. Vehicle type categorization is the separation of vehicles into predefined classes and can be useful for improving transportation efficiency, cost, environmental sustainability, enforcement, safety; and education. ICAN mainly, focuses on the challenging task of differentiating between two-axle vehicles such as passenger car, sports utility vehicle (SUV), van, truck, and bus. This is in contrast to systems that classify according to the number of axles. One characteristic of ICAN is the simplicity, of the 13-neuron one-dimensional neural network, and the employment of a small training set of 13 signatures. The overall classification results of 87% (data set 1) and 82% (data set 2) for seven categories coupled with consistent performance across all vehicle categories was significant and encouraging. Field freeway data was used for testing ICAN representative signatures and video images of different vehicle types are presented.
Keywords:
CLASSIFICATION
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