Transfer learning for vehicle detection using two cameras with different focal lengths

Abstract

This paper proposes a vehicle detection method using transfer learning for two cameras with different focal lengths. A detected vehicle region in an image of one camera is transformed into a binary map. After that, the map is used to filter convolutional neural network (CNN) feature maps which are computed for the other camera's image. We also introduce a robust evolutionary algorithm that is used to compute the relationship between the two cameras in an off-line mode efficiently. We capture video sequences and sample them to make a dataset that includes images with different focal lengths for vehicle detection. We compare the proposed vehicle detection method with baseline detection methods, including faster region proposal networks (Faster-RCNN), single-shot-multi-Box detector (SSD), and detector using recurrent rolling convolution (RRC), in the same experimental context. The experimental results show that the proposed method can detect vehicles at a wide range of distances accurately and robustly, and significantly outperforms the baseline detection methods. (C) 2019 Elsevier Inc. All rights reserved.