X +
Figure 1. Concept of vehicle manoeuvres recognition problem
Once objects are extracted, tracking is accomplished by point-
to-point, edge-to-edge, or area-to-area matching. These
approaches, however, have limitation, because enough
information cannot be derived from single image.
The recognition of objects not only in each image, but also in
successive images is more plausible for the vehicle manoeuvres
recognition. The vehicle manoeuvres recognition is
accomplished in spatio-temporal images. In other words,
problem of interest is identified as forming the region of
vehicles in spatio-temporal images. Figure 1 shows the concept
of vehicle manoeuvres recognition problem. In this sense,
vehicle recognition includes vehicle extraction and vehicle
tracking.
2.2 Vehicle Manoeuvres Recognition Method
Considering the human perception, we will develop a vehicle
manoeuvres recognition method. Visual information has three
features, that are spatial feature, temporal feature and colour
feature. In order to recognize moving objects, spatial-temporal
feature and colour-temporal feature take a important role.
Spatial-temporal feature and colour-temporal feature correspond
to background subtraction and optical flow extraction in image
processing, respectively. Employing these two features, the
pixels that are adjacent and have similar features are grouped.
Consequently, vehicle clusters are formed in the spatio-temporal
image. We call these process as spatio-temporal clustering
method. Figure 2 shows the flow of spatio-temporal clustering
method. Next section will discuss details of the proposed
method.
3. SPATIO-TEMPORAL CLUSTERING METHOD
3.1 Geometric Correction
Although laborious, image sequence must be aligned first of all.
We develop an automatic geometric correction for vehicle
recognition. The most popular method for geometric correction
is to employ Ground Control Points (GCPs). Geometric
correction can be summarized as follows:
(1) GCPs are specified as reference points which should be
clearly perceived and whose coordinates should be known;
(2) GCPs determine a coordinate transformation by the least
squares method.
In this study, we set corners of edge contours of the markers on
the road such as crossings and speed indicators as GCPs. Since
the markers can be restricted in the sense of colour and shape,
| Geometric Correction |
background
image
Y
i background subtraction 1]
| shadow detection
|
| optical flow extraction
i
| spatio-temporal clustering |
features
e Bptical flow
e Background subtraction
value
| vehicle recognition |
Figure 2. Flow of vehicle manoeuvres recognition
SUSAN operator (Smith and Brady, 1997) restricted in colour is
applied to edge detection, and then the edge contours are
eliminated by shape constraints, that are aspect ratio, number of
pixels, and whether closed or not.
Because extracted GCPs are the points of static area basically,
displacement vectors of the GCPs between adjacent images are
very small. Therefore the searching window to find
corresponding points can be limited only within a few pixels
square.
Using the specified GCPs, a coordinate transformation is
determined. In general, Affine transformation or projective
transformation are selected as coordinate transformation. The
transformations assume that the scene is a planer surface
without depth variation. This assumption can be available when
and only when the field-of-view (FOV) of cameras is enough
narrow (typically 5 degree or less) (Irani and Anandan, 1998).
Moreover, the coordinate transformations should coincide GCPs,
which are the corner of markers on the roads, because the
markers affect results of following background subtraction. We
utilize Kriging interpolation as geometric correction so that the
GCPs can be coincided. Kriging denotes a body of techniques
to predict data at arbitrary locations with some observations
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