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

TL;DR

This paper introduces novel dense center-symmetric local binary pattern features for pedestrian detection, demonstrating they are computationally efficient and outperform existing methods like HOG and PHOG on the INRIA dataset.

Contribution

The paper proposes new dense CS-LBP and pyramid CS-LBP/LTP features that are easy to compute and improve pedestrian detection accuracy over state-of-the-art methods.

Findings

Dense CS-LBP features are comparable to HOG with linear SVMs.

Pyramid CS-LBP/LTP features outperform HOG and PHOG.

Combining pyramid CS-LBP with PHOG yields state-of-the-art results.

Abstract

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.