Pedestrian Detection for Android Mobile Devices

Abstract

With the popularity of Android mobile devices and rapid development of hardware performance, more computer vision algorithms can be implemented on Android mobile devices, such as object detection, face recognition, etc. This paper focuses on the problem of implementing pedestrian detection on Android mobile devices. Recently, the commonly used pedestrian detection algorithm is based on sliding window strategy which uses single or multi-features, such as HOG and Channel features. Due to the resource limitation of mobile devices, in this paper, we use HOG and LBP joint feature to achieve pedestrian detection. In order to improve the efficiency of the algorithm, we use the spatial pooling algorithm to process the HOG-LBP joint feature. Experiments on the Android mobile phone shows that this method not only has competitive accuracy but also improves the pedestrian detection efficiency on both INRIA dataset and mobile phone images.