Pyramid Center-Symmetric Local Binary/Trinary Patterns for Effective Pedestrian Detection

Detecting pedestrians in images and videos 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 presents a novel feature, termed pyramid center-symmetric local binary/ternary patterns (pyramid CS-LBP/LTP), for pedestrian detection. The standard LBP proposed by Ojala et al. [1] mainly captures the texture information. The proposed CS-LBP feature, in contrast, captures the gradient information. Moreover, the pyramid CS-LBP/LTP is easy to implement and computationally efficient, which is desirable for real-time applications. Experiments on the INRIA pedestrian dataset show that the proposed feature outperforms the histograms of oriented gradients (HOG) feature and comparable with the start-of-the-art pyramid HOG (PHOG) feature when using the intersection kernel support vector machines (HIKSVMs). 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.