联合足迹识别与监控视频分析的智能刑侦系统

陶一宁; 苏峰; 袁培江; 王田苗; 钟涛; 郝静如

doi:10.13700/j.bh.1001-5965.2020.0062

联合足迹识别与监控视频分析的智能刑侦系统

doi: 10.13700/j.bh.1001-5965.2020.0062

1.
北京麻豆精品秘国产传媒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

基金项目:

国家自然科学基金 61603015

详细信息

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

通讯作者:
袁培江, E-mail: itr@cq5520.com

中图分类号: TP391.4
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- 被引次数: 0
出版历程
- 收稿日期: 2020-03-02
- 录用日期: 2020-04-03
- 网络出版日期: 2020-09-20

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

1.
School of Mechanical Engineering and Automation, Beihang University, Beijing 100083, China
2.
Criminal Investigation Corps, Beijing Public Security Bureau, Beijing 100191, China

Funds:

National Natural Science Foundation of China 61603015

More Information

Corresponding author: YUAN Peijiang, E-mail: itr@cq5520.com

摘要

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。
- 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.
- deep learning /
- big data /
- criminal investigations /
- surveillance video analysis /
- footprint recognition

HTML全文

图 1 基于深度学习的足迹识别技术路线

Figure 1. Footprint recognition technology route based on deep learning

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图 2 足迹图像采样

Figure 2. Sampling of shoeprints

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图 3 降低数据维度过程

Figure 3. Data dimension reduction process

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图 4 归一化处理结果

Figure 4. Results after normalization

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图 5 增广30倍结果

Figure 5. Results after augmented 30 times

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图 6 所有数据中身高的分布

Figure 6. Distribution of height in dataset

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图 7 测试数据中身高与鞋印长度的关系

Figure 7. Correlation between height and length ofshoeprint in test data

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图 8 各种处理条件下的预测结果

Figure 8. Prediction results under various processing conditions

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图 9 模型拟合结果、准确率和误差

Figure 9. Model fitting results, accuracy and deviation

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图 10 线性拟合结果、准确率和误差

Figure 10. Linear fitting results, accuracy and deviation

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图 11 足迹识别系统和监控视频分析的有机结合

Figure 11. Dynamic combination of footprint recognition system and surveillance video analysis

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表 1 三种方法预测结果准确率

Table 1. Accuracy of three methods for predicting results

方法	误差阈值/cm	训练准确率/%	测试准确率/%
本文	5	62.11±0.97	58.98±2.24
本文	10	90.97±0.50	88.38±1.60
经验公式	5		38.31
经验公式	10		66.67
最小二乘法	5		53.09
最小二乘法	10		84.51

下载: 导出CSV

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