与图像目标识别不同，如何平衡速度与精度之间的矛盾一直是行人目标检测面临的主要问题之一。本项目的主要目标是：在不损失目标检测速度的前提下，尽最大可能使用更为丰富的特征集去有效提高目标检测精度。更为详细的，我们拟将Privileged信息概念引入到行人检测领域，将极大丰富的特征集合仅用于模型训练，而非模型预测来有效利用这些特征资源。在此基础上提出基于Privileged信息的行人目标检测算法和框架，并进行相应理论分析。随后在Dollár等人提出的行人目标检测评估体系下对该方法进行有效评价，给出最终实验结论。

Different with object recognition, how to balance the speed and the quality is always a challenging issue in pedestrian detection. The main objective of this project are as follows: without loss of object detection speed, we use richer feature sets to improve object detection accuracy as much as possible. More detail, we intend to introduce the concept of Privileged Information into the pedestrian detection field, and then the rich feature set is only used for model training, rather than the model testing, which will be able to use these features resources more efficiently. On this basis, we proposed the object detection algorithm and framework based on Privileged Information, and make the corresponding theoretical analysis. After that, we will give the final experimental results in the object detection evaluation system framwork proposed by Dollor et al.