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导读：我们知道一篇完整的research proposal需要有Title and abstract、introduction、summary of literature 、The hypothesis and the objectives Methodology、Summary and conclusions、参考文献。以下是一篇用于申请Doctor Application的research proposal提纲，大致的research proposal机构同学们可以参照以下内容：
Abject detection based on deep learning
Abstract摘要
Introduction介绍
本文首先分析了国内外对目标检测算法的研究现状，着重介绍了基于目标特征训练分类器对目标进行分类的广泛应用方法。由于训练后的分类器对目标分类具有较高的误报率，本文提出了一种基于卷积神经网络的行人目标检测算法。
This thesis first analyzes the domestic and foreign research status of object detection algorithm, emphatically introduces the application method which are widely used is based on the object feature trained classifier to classify object. Because of the existing feature of the trained classifier to classify object has high false positives rate, this thesis present a pedestrian object detection algorithm based on convolution neural network on the basis of deep learning.
该算法采用卷积神经网络解决滑动窗效率低的问题，包括两个步骤：（1）可疑行人窗的确认；（2）行人检测。在现有的可疑行人视窗确认中，本文采用融合特征作为行人训练分类器的描述，并以近似于建立分类器金字塔的尺度特征为理想。在检测到的图像上，本文利用不同尺度的滑动窗进行滑动横移，以确定是否存在可疑的行人窗口。在行人检测中，本文利用大量的正负样本进行训练，得到卷积神经网络。为了更好地适应The algorithm consists of two steps in order to solve the low efficiency of sliding window with convolution neural network, (1) the suspected pedestrian window confirmation; (2) the pedestrian detection. In suspected existing pedestrian window confirmation, this thesis use the fusion feature as the description of the pedestrian training classifier and the ideal of nearby scale feature similar to build classifier pyramid. On the inspected images, this thesis use different scales of sliding window to slide traversal to confirm suspected exist pedestrian window. In the pedestrian detection, this thesis rely a large number of positive and negative samples to train and get a convolution neural network. In order to better adept the
行人检测，本论文改进了传统卷积网络的拓扑结构。将可疑行人窗口输入改进的卷积神经网络中，对行人进行检测。pedestrian detection, this thesis improve the topology of traditional convolution network. Input suspected existence of pedestrian’s window into the improved convolution neural network to detect the pedestrian.
Summary of Literature文献综述
Detection algorithm based on template matching基于模板匹配的检测算法
Pedestrian detection algorithm based on classification基于分类的行人检测算法
The hypothesis and the objectives Methodology假设与目标方法论
Suspected pedestrians based on fusion feature window confirmation基于融合特征窗口确认的可疑行人
Pedestrian detection based on Convolutional neural networks基于卷积神经网络的行人检测
Summary and conclusions总结和结论
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