Pedestrian detection in crowded scenes with the histogram of gradients principle
Publikation aus Digital
Sidla O., Rosner M., Lipetski Y.
SPIE - Intelligent Robots and Computer Vision XXIV: Algorithms, Techniques and Active Vision; Boston, USA Oct 2006 , 2006
This paper describes a close to real-time scale invariant implementation
of a pedestrian detector system which is based on the Histogram of
Oriented Gradients (HOG) principle. Salient HOG features are first
selected from a manually created very large database of samples with
an evolutionary optimization procedure that directly trains a polynomial
Support Vector Machine (SVM). Real-time operation is achieved by
a cascaded 2-step classifier which uses first a very fast linear
SVM (with the same features as the polynomial SVM) to reject most
of the irrelevant detections and then computes the decision function
with a polynomial SVM on the remaining set of candidate detections.
Scale invariance is achieved by running the detector of constant
size on scaled versions of the original input images and by clustering
the results over all resolutions. The pedestrian detection system
has been implemented in two versions: i) fully body detection, and
ii) upper body only detection. The latter is especially suited for
very busy and crowded scenarios. On a state-of-the-art PC it is able
to run at a frequency of 8 - 20 frames/sec.