  Accurately detecting pedestrians in images plays a critically important role
in many computer vision applications. Extraction of effective features is the
key to this task. Promising features should be discriminative, robust to
various variations and easy to compute. In this work, we present novel
features, termed dense center-symmetric local binary patterns (CS-LBP) and
pyramid center-symmetric local binary/ternary patterns (CS-LBP/LTP), for
pedestrian detection. The standard LBP proposed by Ojala et al. \cite{c4}
mainly captures the texture information. The proposed CS-LBP feature, in
contrast, captures the gradient information and some texture information.
Moreover, the proposed dense CS-LBP and the pyramid CS-LBP/LTP are easy to
implement and computationally efficient, which is desirable for real-time
applications. Experiments on the INRIA pedestrian dataset show that the dense
CS-LBP feature with linear supporct vector machines (SVMs) is comparable with
the histograms of oriented gradients (HOG) feature with linear SVMs, and the
pyramid CS-LBP/LTP features outperform both HOG features with linear SVMs and
the start-of-the-art pyramid HOG (PHOG) feature with the histogram intersection
kernel SVMs. We also demonstrate that the combination of our pyramid CS-LBP
feature and the PHOG feature could significantly improve the detection
performance-producing state-of-the-art accuracy on the INRIA pedestrian
dataset.
