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

Effective Pedestrian Detection Using Center-symmetric Local Binary/Trinary Patterns