BS 
REFINING CORRECTNESS OF VEHICLE DETECTION AND TRACKING IN AERIAL 
IMAGE SEQUENCES BY MEANS OF VELOCITY AND TRAJECTORY EVALUATION 
D. Lenhart 1 , S. Hinz 2 
'Remote Sensing Technology, Technische Universitaet Muenchen, 80290 Muenchen, Germany 
Dominik.Lenhart@bv.tu-muenchen.de 
"Institute of Photogrammetry and Remote Sensing, University of Karlsruhe, 76128 Karlsruhe, Germany 
Stefan.Hinz@ipf.uni-karlsruhe.de 
KEY WORDS: Traffic Monitoring, Vehicle Trajectories, Aerial Image Sequences, Fuzzy Logic, Evaluation 
ABSTRACT: 
Derivation of statistical traffic data is highly dependent on the balance of detection and false alarm rates. In case false alarms have 
not been eliminated in the initial detection phase, they are often subsequently tracked, though, resulting in trajectories that do not 
match the true traffic situation. This finally leads to derivation of erroneous traffic parameters within the individual road segments. 
In this paper, a method is described how to eliminate false alarms by evaluating the trajectories and velocities of a tracking 
procedure. Basically, two types of false alarms are considered which bias the statistics of traffic data: The first type deals with 
redundant detections that lead to multiple trajectories biasing the statistics. The second type comprises false alarms that belong to the 
static background inducing zero-velocity into the statistics. We show that the presented procedure is able to increase the total 
correctness of detection and tracking from 65% up to 95% which allows a much more precise calculation of traffic flow parameters. 
1. TRAFFIC MONITORING 
The task of collecting wide area traffic parameters plays 
important role in today’s traffic management. Aerial images 
offer a complement source to common measurement systems 
like induction loops and stationary video cameras. Besides 
giving a visual overview, image sequences which cover large 
areas can deliver a time snapshot of a spatially fully covered 
traffic situation of the recorded region. 
In recent years, traffic monitoring using air- and space images 
became more and more attractive mainly due to the availability 
of cost-effective and flexible high-resolution systems mounted 
on aircrafts, i.e. the LUMOS/ANTAR system for traffic 
monitoring (Ernst et al., 2003; Ernst et al. 2005; Ruhe et al., 
2007) or the 3K camera system (Kurz et al., 2007), or on HALE 
platforms and UAVs as presented in the Pegasus project 
(Everaerts et al., 2004). An extensive overview on recent 
developments is given, for instance, in (Stilla et al., 2005; Hinz 
et al., 2006; Lenhart et al., 2008). The following methods are 
especially designed for traffic monitoring with DLR’s 3K 
camera system. This system is able to capture image sequences 
with a frame rate of approx. 3Hz - 7Hz depending on the 
imaging mode (continuous imaging or bursts) with a spatial 
resolution of 20cm - 50cm depending on the flight height. 
Concepts for deriving traffic data from these aerial image 
sequences have been proposed in (Rosenbaum et al. 2008) and 
(Lenhart et al. 2008). The traffic parameters which are 
calculated from image sequences are namely the mean velocity 
and traffic density per road segment. The resulting parameters 
are then integrated into traffic flow models such as the DELPHI 
traffic portal illustrated in (Behrisch et al.). 
2. INFLUENCE OF FALSE ALARMS 
Detection methods as proposed in (Rosenbaum et al. 2008) or 
(Lenhart et al. 2008) deliver a detection quality of about 60% 
completeness and 65-75% correctness. False alarms are mainly 
caused by structures which appear similar to vehicles, like i.e. 
belonging to shadows, road banks etc. 
The influence of the false alarm rate on the calculation of 
generic traffic parameters can be studied using, e.g., Monte- 
Carlo simulations. In the following experiment a dense traffic 
scenario on a multi-lane highway was captured with an image 
sequence and all car trajectories were manually measured in 
this sequence, eventually leading to mean velocity profiles for 
each lane of the highway. Then, a predefined percentage of 
detections were selected at random positions along the road and 
contaminated with a specific percentage of random false alarms. 
Based on these data the velocity profiles were calculated for 
each lane again and compared to the reference data. As the 
estimation of the velocity profile depends strongly on the 
randomly selected positions of the cars, these experiments have 
been carried out 10000 times, in order to gain a certain statistic 
about the quality of the estimated profiles. The following table 
summarizes the RMS values and standard deviations for the 
estimated velocity profiles depending on the respective 
detection and false alarm rate. 
50' 1 detection rate 
5% false alarm rate 
50‘M detection rate 
10% false alarm rate 
50% detection rate 
25% false alarm rate 
RMS 
[km/h] 
a 
[km/h] 
RMS 
[km/h] 
a 
[km/h] 
RMS 
[km/h] 
a 
[km/h] 
5.22 
2.61 
7.03 
4.01 
10.25 
6.27 
30% detection rate 
5% false alarm rate 
30% detection rate 
10% false alarm rate 
30% detection rate 
25% false alarm rate 
RMS 
[km/h] 
a 
[km/h] 
RMS 
[km/h] 
cr 
[km/h] 
RMS 
[km/h] 
a 
[km/h] 
5.97 
3.17 
8.03 
4.66 
11.30 
6.58 
Table 1: Monte-Carlo simulation of reconstruction of velocity 
profiles depending on detection and false alarm rates 
As can be seen, especially the false alarm rate highly influences 
the quality of the estimates. For instance, it is still possible to 
reconstruct the velocity profile up to 6km/h ± 3km/h at a 
detection rate of only 30% when keeping the false alarm rate at 
5%. 
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