Gradient Local Binary Patterns for human detection

Abstract

In recent years, local pattern based features have attracted increasing interest in object detection and recognition systems. Local Binary Pattern (LBP) feature is widely used in texture classification and face detection. But the original definition of LBP is not suitable for human detection. In this paper, we propose a novel feature set named gradient local binary patterns (GLBP), Original GLBP and Improved GLBP, for human detection. Experiments are performed on INRIA dataset, which shows the proposal GLBP feature is more discriminative than histogram of orientated gradient (HOG), histogram of template (HOT) and Semantic Local Binary Patterns (S-LBP), under the same training method. In our experiments, the window size is fixed. That means the performance can be improved by boosting and cascade methods. And the computation of GLBP feature is parallel, which make it easy for hardware acceleration. These factors make GLBP feature possible for real-time human detection.