Intelligent crowd sensing pickpocketing group identification using remote sensing data for secure smart cities

Jing Zhang; Ting Fan; Ding Lang; Yuguang Xu; Hong-an Li; Xuewen Li; Jing Zhang; Ting Fan; Ding Lang; Yuguang Xu; Hong-an Li; Xuewen Li

doi:10.3934/mbe.2023613

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 8: 13777-13797. doi: 10.3934/mbe.2023613

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Intelligent crowd sensing pickpocketing group identification using remote sensing data for secure smart cities

1.
College of Computer Science and Technology, Xi'an University of Science and Technology, Xi'an 710600, China
2.
College of Energy Enginnering, Xi'an University of Science and Technology, Xi'an 710600, China
3.
College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an 710600, China

Received: 23 March 2023 Revised: 27 May 2023 Accepted: 05 June 2023 Published: 16 June 2023

As a public infrastructure service, remote sensing data provided by smart cities will go deep into the safety field and realize the comprehensive improvement of urban management and services. However, it is challenging to detect criminal individuals with abnormal features from massive sensing data and identify groups composed of criminal individuals with similar behavioral characteristics. To address this issue, we study two research aspects: pickpocketing individual detection and pickpocketing group identification. First, we propose an IForest-FD pickpocketing individual detection algorithm. The IForest algorithm filters the abnormal individuals of each feature extracted from ticketing and geographic information data. Through the filtered results, the factorization machines (FM) and deep neural network (DNN) (FD) algorithm learns the combination relationship between low-order and high-order features to improve the accuracy of identifying pickpockets composed of factorization machines and deep neural networks. Second, we propose a community relationship strength (CRS)-Louvain pickpocketing group identification algorithm. Based on crowdsensing, we measure the similarity of temporal, spatial, social and identity features among pickpocketing individuals. We then use the weighted combination similarity as an edge weight to construct the pickpocketing association graph. Furthermore, the CRS-Louvain algorithm improves the modularity of the Louvain algorithm to overcome the limitation that small-scale communities cannot be identified. The experimental results indicate that the IForest-FD algorithm has better detection results in Precision, Recall and F1score than similar algorithms. In addition, the normalized mutual information results of the group division effect obtained by the CRS-Louvain pickpocketing group identification algorithm are better than those of other representative methods.
- crowdsensing,
- intelligent transportation,
- IForest-FD algorithm,
- CRS-Louvain algorithm
Citation: Jing Zhang, Ting Fan, Ding Lang, Yuguang Xu, Hong-an Li, Xuewen Li. Intelligent crowd sensing pickpocketing group identification using remote sensing data for secure smart cities[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 13777-13797. doi: 10.3934/mbe.2023613

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Abstract

As a public infrastructure service, remote sensing data provided by smart cities will go deep into the safety field and realize the comprehensive improvement of urban management and services. However, it is challenging to detect criminal individuals with abnormal features from massive sensing data and identify groups composed of criminal individuals with similar behavioral characteristics. To address this issue, we study two research aspects: pickpocketing individual detection and pickpocketing group identification. First, we propose an IForest-FD pickpocketing individual detection algorithm. The IForest algorithm filters the abnormal individuals of each feature extracted from ticketing and geographic information data. Through the filtered results, the factorization machines (FM) and deep neural network (DNN) (FD) algorithm learns the combination relationship between low-order and high-order features to improve the accuracy of identifying pickpockets composed of factorization machines and deep neural networks. Second, we propose a community relationship strength (CRS)-Louvain pickpocketing group identification algorithm. Based on crowdsensing, we measure the similarity of temporal, spatial, social and identity features among pickpocketing individuals. We then use the weighted combination similarity as an edge weight to construct the pickpocketing association graph. Furthermore, the CRS-Louvain algorithm improves the modularity of the Louvain algorithm to overcome the limitation that small-scale communities cannot be identified. The experimental results indicate that the IForest-FD algorithm has better detection results in Precision, Recall and F1score than similar algorithms. In addition, the normalized mutual information results of the group division effect obtained by the CRS-Louvain pickpocketing group identification algorithm are better than those of other representative methods.

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