Local Binary Patterns for Human Detection on Hexagonal Structure

Abstract

Local binary pattern (LBP) was designed and has been widely used for efficient texture classification. LBP provides a simple and effective way to represent texture patterns. Uniform LBPs play an important role for LBP-based pattern/object recognition as they include majority of LBPs. On the other hand, Human detection based on Mahalanobis distance map (MDM) recognizes appearance of human based on geometrical structure. Each MDM shows a clear texture pattern that can be classified using LBPs. In this paper, we compute LBPs of MDMs on a hexagonal structure. The circular pixel arrangement in hexagonal structure results in higher accuracy for LBP representation than on square structure. Chi-square as a measure is used for human detection based on uniform LBPs obtained. We show that our method using LBPs built on MDMs has a higher human detection rate and a lower false positive rate compared to the method merely based on MDMs. We will also show using experimental results that LBPs on hexagonal structure lead to more robust human classification.