Fast Sliding Window Classification with Convolutional Neural Networks

Abstract

Convolutional Neural Networks (CNNs) have repeatedly been shown to be the state of the art method for natural signal classification -- image classification in particular. Unfortunately, due to the high model complexity CNNs often cannot be used for object detection tasks with real-time constraints, where many predictions have to be made on sub-windows of a large input image. We demonstrate how two recent advances in CNN efficiency can be combined, with modifications, to provide a substantial speedup for sliding window classification. An in depth analysis of the various factors that can impact performance is presented.