Vehicle: Vehicle Silhouettes

VehicleR Documentation

Vehicle Silhouettes


The purpose is to classify a given silhouette as one of four types of vehicle, using a set of features extracted from the silhouette. The vehicle may be viewed from one of many different angles. The features were extracted from the silhouettes by the HIPS (Hierarchical Image Processing System) extension BINATTS, which extracts a combination of scale independent features utilising both classical moments based measures such as scaled variance, skewness and kurtosis about the major/minor axes and heuristic measures such as hollows, circularity, rectangularity and compactness.

Four "Corgie" model vehicles were used for the experiment: a double decker bus, Cheverolet van, Saab 9000 and an Opel Manta 400. This particular combination of vehicles was chosen with the expectation that the bus, van and either one of the cars would be readily distinguishable, but it would be more difficult to distinguish between the cars.


data("Vehicle", package = "mlbench")


A data frame with 846 observations on 19 variables, all numerical and one nominal defining the class of the objects.

[,1] Comp Compactness
[,2] Circ Circularity
[,3] D.Circ Distance Circularity
[,4] Rad.Ra Radius ratio
[,5] Pr.Axis.Ra pr.axis aspect ratio
[,6] Max.L.Ra max.length aspect ratio
[,7] Scat.Ra scatter ratio
[,8] Elong elongatedness
[,9] Pr.Axis.Rect pr.axis rectangularity
[,10] Max.L.Rect max.length rectangularity
[,11] Sc.Var.Maxis scaled variance along major axis
[,12] Sc.Var.maxis scaled variance along minor axis
[,13] Ra.Gyr scaled radius of gyration
[,14] Skew.Maxis skewness about major axis
[,15] Skew.maxis skewness about minor axis
[,16] Kurt.maxis kurtosis about minor axis
[,17] Kurt.Maxis kurtosis about major axis
[,18] Holl.Ra hollows ratio
[,19] Class type


  • Creator: Drs.Pete Mowforth and Barry Shepherd, Turing Institute, Glasgow, Scotland.

These data have been taken from the UCI Repository Of Machine Learning Databases (Blake & Merz 1998) and were converted to R format by Evgenia Dimitriadou in the late 1990s.

The current version of the UC Irvine Machine Learning Repository Statlog Vehicle Silhouettes data set is available from \Sexpr[results=rd]{tools:::Rd_expr_doi("10.24432/C5HG6N")}.


Turing Institute Research Memorandum TIRM-87-018 "Vehicle Recognition Using Rule Based Methods" by Siebert,JP (March 1987)

Blake, C.L. & Merz, C.J. (1998). UCI Repository of Machine Learning Databases. Irvine, CA: University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘⁠⁠’.


data("Vehicle", package = "mlbench")

mlbench documentation built on May 29, 2024, 4:49 a.m.

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