Deep infrared pedestrian classification based on automatic image matting
Deep infrared pedestrian classification based on automatic image matting
复制标题
基于自动抠图的深度红外行人分类
DOI：
10.1016/j.asoc.2019.01.024
复制
发表时间：
2019-04-01
影响因子：
8.7
通讯作者：
Tan, Kay Chen
中科院分区：
文献类型：
--
作者：
Liang, Yihui;Huang, Han;Tan, Kay Chen
红外行人分类在高级驾驶辅助系统中起着重要作用。然而，当行人图像叠加在杂乱的背景上时，会遇到很大的困难。许多研究者设计了深度神经网络来从杂乱的背景中对行人进行分类。然而，一个非常深的神经网络有很高的计算成本。对杂乱背景的抑制可以在不增加深度的情况下提高深度神经网络的性能，但在过去很少受到重视。本研究提出了一种用于红外行人的自动图像抠图方法，该方法抑制了杂乱的背景，并为深度学习提供了一致的输入。应用行人分类领域的专业知识，从背景杂乱的图像中自动、柔和地提取前景对象。本研究根据行人头部和上身的估计位置生成trimaps，无需任何用户交互，而传统方法必须手动生成trimaps。我们采用全局抠图的方法实现图像抠图，并将生成的图作为输入。行人的表示是通过深度学习方法从生成的alpha mattes中发现的，其中杂乱的背景被抑制，前景被增强。实验结果表明，该方法在计算成本可以忽略不计的情况下，提高了当前最先进的深度学习方法的红外行人分类性能。(C) 2019 Elsevier B.V.版权所有
Infrared pedestrian classification plays an important role in advanced driver assistance systems. However, it encounters great difficulties when the pedestrian images are superimposed on a cluttered background. Many researchers design very deep neural networks to classify pedestrian from cluttered background. However, a very deep neural network associated with a high computational cost. The suppression of cluttered background can boost the performance of deep neural networks without increasing their depth, while it has received little attention in the past. This study presents an automatic image matting approach for infrared pedestrians that suppresses the cluttered background and provides consistent input to deep learning. The domain expertise in pedestrian classification is applied to automatically and softly extract foreground objects from images with cluttered backgrounds. This study generates trimaps, which must be generated manually in conventional approaches, according to the estimated positions of pedestrian's head and upper body without the need for any user interaction. We implement image matting by adopting the global matting approach and taking the generated trimap as an input. The representation of pedestrian is discovered by a deep learning approach from the resulting alpha mattes in which cluttered background is suppressed, and foreground is enhanced. The experimental results show that the proposed approach improves the infrared pedestrian classification performance of the state-of-the-art deep learning approaches at a negligible computational cost. (C) 2019 Elsevier B.V. All rights reserved.