基于自动车牌识别数据的团伙犯罪时空关联车辆发现方法

A Spatio-Temporally Associated Vehicle Discovery Method for Group-Crime Based on ANPR Data

自动车牌识别(ANPR)数据是当前公安工作中获取车辆轨迹的主要来源之一,基于ANPR数据发现涉案车辆的时空关联车辆,对于团伙犯罪防控具有重要意义。在实际工作中发现,团伙车辆会存在主观避嫌意图,导致出现故意远距离跟随等与传统伴随关系不同的时空关联模式,而现有方法难以有效识别,为此,本文基于车牌自动识别(ANPR)数据,提出了团伙犯罪的时空关联车辆发现方法。①通过分析团伙车辆的跟随策略,梳理出“近距离跟随模式”、“故意远距离跟随模式”、“另择路线前往模式”3种车辆时空关联模式;②基于ANPR数据构建关联车辆数据模型,并提出车辆关联的时空约束参数;在此基础上,提出了时空关联车辆的发现方法;③以B城市为例,采用团伙犯罪车辆的相关ANPR数据进行试验与分析,基于团伙案件历史数据对时空约束参数阈值进行定量评估。在此基础上,对某典型案件进行时空关联车辆分析,将本文方法与频繁序列挖掘、计算伴随概率2种方法对比,本文方法有效率平均可达87.59%,优于对比方法,试验结果表明,本文方法能够有效发现故意远距离跟随、另择路线前往等传统方法难以发现的时空关联车辆,能够为公安部门开展团伙犯罪防控工作提供新思路和技术支持。

This paper addresses the challenge of discovering spatio-temporally associated vehicles involved in crimes using Automatic Number Plate Recognition(ANPR)data,which is a crucial resource in public security work for obtaining vehicle trajectories.The significance of identifying associated vehicles in the context of group-crime prevention and control is emphasized.Practical experiences reveal that criminal groups often adopt subjective strategies to avoid suspicion,leading to unique spatio-temporal association patterns such as intentional long-distance following,which differ from traditional accompanying relationships and are difficult to detect with existing methods.Oriented to the actual needs of public security work,from the perspective of group-crime,to tackle this issue,the paper first analyzes the travel patterns of criminal group vehicles and categorizes them into three main spatio-temporal association modes:close-following mode,intentional long-distance following mode,and alternative-route mode.These modes reflect the different strategies used by criminals to avoid detection,ranging from maintaining close proximity to the peer vehicle to deliberately choosing different routes.Based on these patterns,the paper develops a data model using ANPR data.The study introduces spatio-temporal constraint parameters to better capture the association relationships between vehicles.These parameters include the monitoring point time constraint(Δti),point accompanying number(Num_Wx),continuous point accompanying number(Con_Num_Wx),intermittent accompanying distance(d),and accompanying duration(δt).The proposed method for discovering spatio-temporally associated vehicles leverages these parameters to identify potential criminal associations.The methodology involves preprocessing ANPR data to obtain vehicle trajectories,extracting candidate accompanying vehicle sets,calculating spatio-temporal constraint parameters for each candidate vehicle,and setting thresholds for these parameters to discover associated vehicles containing different spatio-temporal patterns.Finally,taking city B as an example,the relevant ANPR data of group-crimes vehicles are used for test and analysis,and the spatio-temporal constraint parameter thresholds are quantitatively evaluated based on the historical data of group-crime cases,based on which the spatio-temporal correlation vehicle analysis of a typical case is conducted,and when comparing this paper's method with the two methods of frequent sequence mining and calculating the concomitant probability,the effectiveness of this paper's method can reach up to 87.59%on average,which is better than the the comparison methods.The results show that the method can effectively identify vehicles engaged in long-distance following and alternative-route strategies,which are often missed by traditional methods.As a result,it is able to quickly target those involved in the crime and further narrow the scope of investigation.In conclusion,the paper presents a comprehensive method for discovering spatio-temporally associated vehicles using ANPR data,significantly enhancing the ability to detect vehicles with complex association patterns.This method not only broadens the application scope of spatiotemporal association discovery but also provides new insights and technical support for public security departments in addressing group-crimes.