为了有效地检测红外图像中的行人,提出了一种基于神经网络的检测方法。采用三维中值滤波来提取背景图像，用图像差分法提取目标区域(regions of interest, ROIs)，提出傅里叶描述子作为目标区域形状特征；设计了BP神经网络分类器并用来对ROIs进行识别。通过大量不同种类不同形状的样本对分类器进行验证，结果表明该分类器具有较高的识别率和较低的虚警率，能够快速有效地识别ROIs, 具有良好的分类能力。
Abstract:
A method based neural network for pedestrians detection in infrared images is presented. The background image is extracted adaptively with 3D median filtering and regions of interest (ROIs) are segmented out with the image difference method. The Fourier descriptors (FD) are employed as the shape feature of ROIs. A BP neural network is designed to recognize ROIs. The classifier is tested with a lot of samples with different types and different shapes. The experimental results show that the classifier has high true positive rate and low false positive rate, which can distinguish whether the ROIs are pedestrians or not effectively and efficiently.
