MKL-SVM-based human detection for autonomous navigation of a robot

This paper presents a classifier trained by a multiple kernel-learning support vector machine (MKL-SVM) to detect a human in sequential images from a video stream. The developed method consists of two aspects: multiple features consisting of HOG features and HOF features suitable for moving objects, and combined nonlinear kernels for SVM. For the purpose of real time application in autonomous navigation, the SimpleMKL algorithm is implemented into the proposed MKL-SVM classifier. It is able to converge rapidly with comparable efficiency through a weighted 2-norm regularization formulation with an additional constraint on the weights. The classifier is compared with the state-of-the-art linear SVM using a dataset called TUD-Brussels, which is available on line. The results show that the proposed classifier outperforms the Linear SVM with respect to accuracy.