Thermal pedestrian detection using block LBP with multi-level classifier

Pedestrian detection is an important topic in many applications, such as intelligent transportation systems (ITSs) or surveillance. For the purpose of applications used around the clock, the work for detecting pedestrian based on thermal sensors has attracted significant attention. To achieve this, this paper proposes a LBP (local binary pattern) encoded multi-level classifier for detecting pedestrians in thermal images. The proposed method consists of training and detection stages. The training stage firstly encodes the texture information of all training samples in all possible blocks by using LBP. For a particular block, the patches of all positive and negative samples with the same LBP code are collected to train a SVM (support vector machine) classifier for discrimination. HOGs (Histogram of Oriented Gradients) are used for the description of the patch appearance. By considering a set of learned SVMs for a particular block as a weak classifier, a set of discriminative weak classifiers is selected to form a final classifier (strong classifier) by using Adaboost. The experimental results on the constructed dataset demonstrate that the proposed method outperforms those in the literature.