One of the most sought-after research areas in object detection is pedestrian detection owing to its applications especially in automated surveillance and robotics. Traditional methods use hand-crafted features to characterize pedestrians. In this work, we have pro-posed a new hand-crafted feature extraction method that concatenates shape, color and texture features; which is then classified by using Support Vector Machine (SVM). As in recent years, deep learning models such as Convolutional Neural Networks (CNNs) have become an eminent state of the art in detection challenges, which unlike the manually designed feature extraction mechanism, results in more accuracy. Therefore, we have also proposed a CNN network, a modification of the pre-trained ResNet-18 named as Multi-layer Feature Fused-ResNet (MF2-ResNet). We have used the proposed modification for (1) feature extraction; which is then classified by using Support Vector Machine (SVM); (2) End-to-End feature extraction and classification by the CNN network and (3) MF2-ResNet based Faster-RCNN to include region proposals in the detection pipeline. To evaluate the proposed method, it is compared with existing pre-trained CNNs. The MF2-ResNet based Faster R-CNN is compared with state-of-the-art detection methods. Three benchmark pedestrian datasets are used in this work: INRIA, NICTA and Daimler.

中文翻译：

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基于手工特征和多层特征 Fused-ResNet 模型的行人检测

对象检测中最受欢迎的研究领域之一是行人检测，因为它的应用尤其是在自动监控和机器人技术中。传统方法使用手工制作的特征来表征行人。在这项工作中，我们提出了一种新的手工特征提取方法，将形状、颜色和纹理特征连接起来；然后使用支持向量机（SVM）对其进行分类。近年来，卷积神经网络 (CNN) 等深度学习模型在检测挑战中已成为最先进的技术，与手动设计的特征提取机制不同，它具有更高的准确性。因此，我们还提出了一个 CNN 网络，它是对预训练的 ResNet-18 的修改，称为多层特征 Fused-ResNet (MF2-ResNet)。我们已将建议的修改用于（1）特征提取；然后使用支持向量机（SVM）对其进行分类；(2) CNN 网络的端到端特征提取和分类和 (3) 基于 MF2-ResNet 的 Faster-RCNN，将区域提议包含在检测管道中。为了评估所提出的方法，将其与现有的预训练 CNN 进行比较。将基于 MF2-ResNet 的 Faster R-CNN 与最先进的检测方法进行了比较。这项工作使用了三个基准行人数据集：INRIA、NICTA 和戴姆勒。它与现有的预训练 CNN 进行了比较。将基于 MF2-ResNet 的 Faster R-CNN 与最先进的检测方法进行了比较。这项工作使用了三个基准行人数据集：INRIA、NICTA 和戴姆勒。它与现有的预训练 CNN 进行了比较。将基于 MF2-ResNet 的 Faster R-CNN 与最先进的检测方法进行了比较。这项工作使用了三个基准行人数据集：INRIA、NICTA 和戴姆勒。