2014-01-06T22:23:10Z
23
2014-01-06T22:23:10Z
public
vehicle
ARFF
1
Class
1
20914
Public
active
1
https://www.openml.org/data/download/54/dataset_54_vehicle.arff
vehicle
**Author**: Dr. Pete Mowforth and Dr. Barry Shepherd
**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/Statlog+(Vehicle+Silhouettes))
**Please cite**: Siebert,JP. Turing Institute Research Memorandum TIRM-87-018 "Vehicle Recognition Using Rule Based Methods" (March 1987)
NAME
vehicle silhouettes
PURPOSE
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.
PROBLEM TYPE
classification
SOURCE
Drs.Pete Mowforth and Barry Shepherd
Turing Institute
George House
36 North Hanover St.
Glasgow
G1 2AD
CONTACT
Alistair Sutherland
Statistics Dept.
Strathclyde University
Livingstone Tower
26 Richmond St.
GLASGOW G1 1XH
Great Britain
Tel: 041 552 4400 x3033
Fax: 041 552 4711
e-mail: alistair@uk.ac.strathclyde.stams
HISTORY
This data was originally gathered at the TI in 1986-87 by
JP Siebert. It was partially financed by Barr and Stroud Ltd.
The original purpose was to find a method of distinguishing
3D objects within a 2D image by application of an ensemble of
shape feature extractors to the 2D silhouettes of the objects.
Measures of shape features extracted from example silhouettes
of objects to be discriminated were used to generate a class-
ification rule tree by means of computer induction.
This object recognition strategy was successfully used to
discriminate between silhouettes of model cars, vans and buses
viewed from constrained elevation but all angles of rotation.
The rule tree classification performance compared favourably
to MDC (Minimum Distance Classifier) and k-NN (k-Nearest Neigh-
bour) statistical classifiers in terms of both error rate and
computational efficiency. An investigation of these rule trees
generated by example indicated that the tree structure was
heavily influenced by the orientation of the objects, and grouped
similar object views into single decisions.
DESCRIPTION
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.
The images were acquired by a camera looking downwards at the
model vehicle from a fixed angle of elevation (34.2 degrees
to the horizontal). The vehicles were placed on a diffuse
backlit surface (lightbox). The vehicles were painted matte black
to minimise highlights. The images were captured using a CRS4000
framestore connected to a vax 750. All images were captured with
a spatial resolution of 128x128 pixels quantised to 64 greylevels.
These images were thresholded to produce binary vehicle silhouettes,
negated (to comply with the processing requirements of BINATTS) and
thereafter subjected to shrink-expand-expand-shrink HIPS modules to
remove "salt and pepper" image noise.
The vehicles were rotated and their angle of orientation was measured
using a radial graticule beneath the vehicle. 0 and 180 degrees
corresponded to "head on" and "rear" views respectively while 90 and
270 corresponded to profiles in opposite directions. Two sets of
60 images, each set covering a full 360 degree rotation, were captured
for each vehicle. The vehicle was rotated by a fixed angle between
images. These datasets are known as e2 and e3 respectively.
A further two sets of images, e4 and e5, were captured with the camera
at elevations of 37.5 degs and 30.8 degs respectively. These sets
also contain 60 images per vehicle apart from e4.van which contains
only 46 owing to the difficulty of containing the van in the image
at some orientations.
ATTRIBUTES
COMPACTNESS (average perim)**2/area
CIRCULARITY (average radius)**2/area
DISTANCE CIRCULARITY area/(av.distance from border)**2
RADIUS RATIO (max.rad-min.rad)/av.radius
PR.AXIS ASPECT RATIO (minor axis)/(major axis)
MAX.LENGTH ASPECT RATIO (length perp. max length)/(max length)
SCATTER RATIO (inertia about minor axis)/(inertia about major axis)
ELONGATEDNESS area/(shrink width)**2
PR.AXIS RECTANGULARITY area/(pr.axis length*pr.axis width)
MAX.LENGTH RECTANGULARITY area/(max.length*length perp. to this)
SCALED VARIANCE (2nd order moment about minor axis)/area
ALONG MAJOR AXIS
SCALED VARIANCE (2nd order moment about major axis)/area
ALONG MINOR AXIS
SCALED RADIUS OF GYRATION (mavar+mivar)/area
SKEWNESS ABOUT (3rd order moment about major axis)/sigma_min**3
MAJOR AXIS
SKEWNESS ABOUT (3rd order moment about minor axis)/sigma_maj**3
MINOR AXIS
KURTOSIS ABOUT (4th order moment about major axis)/sigma_min**4
MINOR AXIS
KURTOSIS ABOUT (4th order moment about minor axis)/sigma_maj**4
MAJOR AXIS
HOLLOWS RATIO (area of hollows)/(area of bounding polygon)
Where sigma_maj**2 is the variance along the major axis and
sigma_min**2 is the variance along the minor axis, and
area of hollows= area of bounding poly-area of object
The area of the bounding polygon is found as a side result of
the computation to find the maximum length. Each individual
length computation yields a pair of calipers to the object
orientated at every 5 degrees. The object is propagated into
an image containing the union of these calipers to obtain an
image of the bounding polygon.
NUMBER OF CLASSES
4 OPEL, SAAB, BUS, VAN
NUMBER OF EXAMPLES
Total no. = 946
No. in each class
opel 240
saab 240
bus 240
van 226
100 examples are being kept by Strathclyde for validation.
So StatLog partners will receive 846 examples.
NUMBER OF ATTRIBUTES
No. of atts. = 18