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‘’Land Planning Dept., University of Calabria, 87036 Rende, Italy - g.artese@unical.it 
Commission III - WG III/5 
KEY WORDS: Digital Photogrammetry, Vehicle Tracking, Kalman Filter, Change Detection 
ABSTRACT: 
For the vehicle detection and tracking in the roundabouts, several difficulties have to be faced, e.g. the use of non nadiral 
perspectives, vehicle images overlapping and radiometric differences between top and side of the cars: the silhouette of the cars is, 
besides, not regular and the size is variable. In the paper, the upgrade of a procedure for detecting and tracking vehicles in a 
roundabout is presented. The image subtraction technique is used, along with geometric and radiometric filters. The problems due to 
the variation of the background radiometric characteristics are discussed. Kalman filter has been used to foreseen the position of 
vehicles for a better tracking, and to improve the determination of the trajectories. The results of a test are presented. 
1. INTRODUCTION 
Object detection and tracking can be very' useful in a range of 
industries and applications, like surveillance, vehicle and 
pedestrian tracking. Trajectory and velocity estimation of 
vehicles is very important for the management of traffic, above 
all in the urban zones and for road intersections. 
The most used methods for traffic monitoring allow to obtain 
the number of vehicles crossing a given section of a road; 
automatic sensors (pressure hose sensors, magnetic buried 
loops) have been used for decades, to automatically count the 
axes or the vehicles, but no information about trajectories are 
generally obtained. 
Several procedures, based on digital photogrammetry 
techniques and computer vision have been proposed in the last 
years for the estimation of traffic flow. New available 
technologies have been also used, and multisensor systems have 
been tested (Grejner-Brzezinska et al„ 2007, Toth and Grejner- 
Brzezinska, 2007. Yao et al„ 2009, Goyat et al., 2009). The 
main advantage of the computer vision techniques is the 
possibility to obtain information regarding not only the number 
of entering and outgoing vehicles, but also their trajectories. 
Some authors proposed algorithms set up by using a single 
camera (Reulke et al., 2002, Broggi and Dickmanns, 2000) or 
multi-camera systems (Pedersini et al., 2001). In more complex 
systems, images obtained with infrared cameras are also used 
(Dalaff et al., 2003, Hinz and Stilla, 2006, Kirchhof and Stilla, 
2006). The detection of vehicles is generally performed by 
using procedures based on the segmentation of the groups of 
pixels having similar chromatic values, and on the edges 
extraction. Morphologic operations (regions filling and erosion, 
edges dilatation) can be performed to optimize the results. 
Depending on the kind of sensor data, a data base with the 
characteristics of the vehicles can be used for facilitating the 
recognition, while geometric conditions, such as the parallelism 
between street edge and vehicle trajectories can be imposed 
only for straight roads (Puntavungkour and Shibasaki. 2003). 
If the differences of images are used, the frames obtained by a 
camera are compared to detect the presence and the position of 
the vehicles. One can follow essentially two ways: to compare 
every frame with the previous one, or to compare every frame 
with a base frame (background) without vehicles. 
Due to the short time interval between two consecutive frames, 
the background is practically identical in the first case, and it is 
possible to detect the vehicles in motion, but not those 
immobile ones; if this way is followed, the block-based motion 
estimation is usually chosen, by using high performance 
computers (Min Tan and Siegel, 1999). Correlation techniques 
are used, by subdividing two consecutive frames in small 
blocks, and by obtaining for every block the movement vector: 
a neighbourhood window of pixels in a given image, centred on 
a specific pixel, is searched over a larger neighbourhood 
window of pixels in the previous image, centred on the same 
pixel. It is assumed that from frame-to-frame in a video 
sequence pixel intensity values do not change and that the video 
source is stationary. The position where the minimum absolute 
or squared difference is obtained, gives the motion vector for 
the centre pixel. To save run time, the block-based motion 
estimation is run only for a subset of pixels in the image and the 
results are interpolated over the entire image. 
If a base frame is used, it is possible to detect both mobile or 
immobile vehicles, but some problems must be solved, 
essentially due to: 
- variations of the lighting conditions, that make practically 
impossible to use a single base frame: 
- presence of noises in the images; 
- shadows projected from the vehicles, trees and surrounding 
buildings; 
- movements of the video camera (oscillations and spins, even 
if modest, make the elimination of the background difficult and 
create false moving objects). 
Other difficulties to overcome are the superimposition of the 
images of close vehicles and the necessity of a real time 
elaboration. 
A key element for many target tracking algorithms is an 
accurate background subtraction (Seki and Wada, 2003, 
Isenegger et al., 2005). An overview of several techniques for 
background detection and updating has been made by Cheung 
and Kamath (2004). In this paper, simple Frame Difference 
(FD), Median Filter (MF) (Cucchiara at al., 2003), 
Approximated Median Filter (AMF) (McFarlane and Schofield, 
1995) , Kalman Filter (KF) and Mixture of Gaussian (MoG) 
(Friedman and Russell, 1997, Stauffer and Grimson, 1999) are 
compared. The best results are obtained by MoG and MF, 
followed by AMF. In spite of its simple implementation, low 
storage requirements and computing rapidity, Approximated 
Median Filter shows good performances. Also the Running 
Gaussian Average (RGA) (Wren et al., 1997) can be used with 
good results. More sophisticated approaches, like Bayesian 
Background Estimation (Rahimizadeh et al., 2009) or 
multimodal information integration (Kato and Wada, 2004) has 
been proposed. Kalman Filter for background detection has 
been implemented in commercial software packages (MVTec , 
2009). 
In the following, a technique for detecting and tracking vehicles 
in a roundabout is described. Image subtraction, geometric and 
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