北京航空航天大学学报 ›› 2020, Vol. 46 ›› Issue (9): 1730-1738.doi: 10.13700/j.bh.1001-5965.2020.0062

• 论文 • 上一篇    下一篇

联合足迹识别与监控视频分析的智能刑侦系统

陶一宁1, 苏峰1, 袁培江1, 王田苗1, 钟涛2, 郝静如2   

  1. 1. 北京航空航天大学 机械工程及自动化学院, 北京 100083;
    2. 北京市公安局刑事侦查总队, 北京 100006
  • 收稿日期:2020-03-02 发布日期:2020-09-22
  • 通讯作者: 袁培江 E-mail:itr@buaa.edu.cn
  • 作者简介:陶一宁 男,硕士研究生。主要研究方向:计算机视觉、机器学习;袁培江 男,博士,副教授,硕士生导师。主要研究方向:计算机视觉、机器人。
  • 基金资助:
    国家自然科学基金(61603015)

Intelligent criminal investigation system based on both footprint recognition and surveillance video analysis

TAO Yining1, SU Feng1, YUAN Peijiang1, WANG Tianmiao1, ZHONG Tao2, HAO Jingru2   

  1. 1. School of Mechanical Engineering and Automation, Beihang University, Beijing 100083, China;
    2. Criminal Investigation Corps, Beijing Public Security Bureau, Beijing 100191, China
  • Received:2020-03-02 Published:2020-09-22
  • Supported by:
    National Natural Science Foundation of China (61603015)

摘要: 刑侦破案是打击违法犯罪和确保国家长治久安的基本要求。刑侦破案的一大关键是如何有效地利用采集到的信息。为了更好地配合刑侦工作,提出了联合足迹识别与监控视频分析的智能刑侦系统。该系统基于深度学习方法,首先根据足迹信息,包括鞋印长度、宽度、受力分布情况、步长、步幅等,利用卷积神经网络技术实现嫌疑人个人特征的预测;其次联合周边监控视频大数据进行智能分析比较,利用大数据技术快速处理信息,分析视频中行人的个人特点;最后运用虚拟现实仿真技术构建足部压力和鞋底受力分析有限元模型,利用模型获得各种复杂场景下的仿真足迹。三者相互印证,有机结合,快速筛选刑侦对象。实验结果表明,该系统可以高效准确地根据足迹特征实现身高预测,并且与视频监控大数据相结合,可以迅速缩小排查范围并锁定凶手。

关键词: 深度学习, 大数据, 刑侦破案, 监控视频分析, 足迹识别

Abstract: Criminal investigation is the basic requirement for cracking down on crimes and ensuring the long-term security of the country. One of the key points in criminal investigation is how to effectively use the collected information. In order to support criminal investigation better, an intelligent criminal investigation system based on both footprint recognition and surveillance video analysis is proposed. The system is powered by deep learning methods. First, using convolutional neural network technique, it predicts the characteristics of suspected individuals based on footprint information, including the footprint length, width, distribution of force, step length, stride, etc. Second, it uses the surrounding surveillance video big data for intelligent comparison to quickly screen criminal investigation objects and analyze the personal characteristics of pedestrians. Finally, the virtual reality simulation technology is used to construct a finite element model of foot pressure and sole stress analysis, and the model is used to obtain simulation footprints in various complex scenarios. The three confirmed each other and organically combined to quickly select criminal investigation targets. Experimental results show that the system can efficiently and accurately achieve people height prediction based on footprint characteristics. Combined with video surveillance big data, the proposed system can quickly narrow down the investigation range and find out the killer.

Key words: deep learning, big data, criminal investigations, surveillance video analysis, footprint recognition

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