Vehicle detection methods for surveillance applications
Publication from Digital
Sidla O., Wildling E., Lipetski Y.
SPIE Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques, and Active Vision; Boston, USA Oct 2006 , 2006
The efficient monitoring of traffic flow as well as related surveillance
and detection applications demand an increasingly robust recognition
of vehicles in image and video data. This paper describes two different
methods for vehicle detection in real world situations: Principal
Component Analysis and the Histogram of Gradients principle. Both
methods are described and their detection capabilities as well as
advantages and disadvantages are compared. A large sample dataset
which contains images of cars from the backside and frontside in
day and night conditions is the basis for creating and optimizing
both variants of the proposed algorithms. The resulting two detectors
allow recognition of vehicles in frontal view +- 30 deg and views
from behind +- 30 deg. The paper demonstrates that both detection
methods can operate effectively even under difficult lighting situations
with high detection rates and a low number of false positives.