Fast object detection based on color histograms and local binary patterns

In this paper, we present a fast object detection algorithm based on color histograms and local binary patterns (LBP). The proposed method consists of two steps: coarse target object detection and precise target object detection. During the coarse target object detection step, a small number of target candidates were generated using integral color histogram matching. To reduce computational complexity, we only used the dominant color of the integral color histogram matching. During the precise target object detection step, the target object was determined by comparing LBP feature distributions with those of the target object candidates. Experimental results show that the proposed method is efficient and robust.

I. Introduction

The goal of object detection is to find the position of a target object in an image. Object detection algorithms should be accurate and fast enough for real time applications. Feature selection is one of the most important steps for object detection. For high accuracy and real time object detection, features should be discriminative, robust, and easy to compute.