Role In Public Security Research Articles

SDIGRU: Spatial and Deep Features Integration Using Multilayer Gated Recurrent Unit for Human Activity Recognition

Smart video surveillance plays a significant role in public security via storing a huge amount of continual stream data, evaluates them, and generates warns where undesirable human activities are performed. Recognition of human activities in video surveillance has faced many challenges such as optimal evaluation of human activities under growing volumes of streaming data with complex computation and huge time processing complexity. To tackle these challenges we introduce a lightweighted spatial-deep features integration using multilayer GRU (SDIGRU). First, we extract spatial and deep features from frames sequence of realistic human activity videos via utilizing a lightweight MobileNetV2 model and then integrate those spatial-deep features. Although deep features can be used for human activity recognition, they contain only the high-level appearance, which is insufficient to correctly identify the particular activity of human. Thus, we jointly apply deep information with spatial appearance to produce detailed level information. Furthermore, we select rich informative features from spatial-deep appearances. Then, we train multilayer gated recurrent unit (GRU) and feed informative features to learn the temporal dynamics of human activity frames sequence at each time step of GRU. We conduct our experiments on benchmark YouTube11, HMDB51, and UCF101 datasets of human activity recognition. The empirical results show that our method achieved significant recognition performance with low computational complexity and quick response. Finally, we compare the results with existing state-of-the-art techniques, which show the effectiveness of our method.

Crowd Counting Based on Context-Aware and Multi Scale Feature Fusion

Crowd counting plays an important role in public security. Estimating the number of people in an image with congested crowd accurately is a challenging task. The crowd counting method based on fully convolutional network can perform well in crowd image with complex scene. In this paper, to address the counting problems of occlusion，background clutter and perspective effect, we proposed a simple but effective method called Context-aware Multi scale Fusion Network(CMF Net).The CMF Net applied VGG network as backbone to extract coarse features. Then, three context-aware multi-scale fusion modules (CMFM) are adopted. Each CMFM consist of multi-scale feature extraction module (MEM) and context-aware feature extraction module (CEM). In addition, we propose adaptively dense connection to promoted information transmission in the counting network. Experiments on four datasets demonstrate that our network achieves competitive and effective results.

FIGAT: Accurately Classify Individual Crime Risks with Multi-information Fusion

Crime prediction plays a vital role in public security. Existing studies infer crime locations or crime groups without considering individual GPS trajectory data. They ignore joint influence on crime patterns coming from the internal relationship between criminals, locations, and time. In this study, we propose Fusion Information Graph Attention Networks (FIGAT), which classifies individuals into high and low risks with personal movement time series and location trajectories. To solve the independence of individual crime behavior and the fusion information loss problem, FIGAT proposes Multi-dimension Fusion Information Graph to combine semantic correlation features with conventional person basic features, time features, and location features. FIGAT constructs a multi-relation graph attention layer, which utilizes the semantic relationship and node information to accurately classify individuals into high and low risks. We evaluate FIGAT with 14,625,884 GPS trajectories from 1038 individuals collected by a real-world public safety department. The results demonstrate that FIGAT improves F1 score by 41%, 32%, and 23% compared with legacy machine learning, RNN-based deep learning, and graph neural network SOTA methods, respectively. T-SNE results and ablation experiments further prove the effectiveness of FIGAT.

A survey of video violence detection

ABSTRACT As one of the important applications of intelligent video surveillance, violent behaviour detection (VioBD) plays a crucial role in public security and safety. As a particular type of behaviour recognition, VioBD aims to identify whether the behaviours that occurred in the scene is aggressive, such as fighting and assault. To comprehensively analyse the current state and predict the future trend of VioBD research, we survey the existing approaches of VioBD in this work. First, we briefly introduce the basic principle and the challenges of VioBD; Then, we category the existing approaches according to their framework, including the traditional framework, end-to-end deep learning framework, and hybrid deep learning framework. Finally, we introduce the public datasets for evaluating the performance of VioBD approaches and compare their performances on these datasets. Besides, we also summarise the open problems in VioBD and predict its future trends.

Towards Efficient Video Detection Object Super-Resolution with Deep Fusion Network for Public Safety

Video surveillance plays an increasingly important role in public security and is a technical foundation for constructing safe and smart cities. The traditional video surveillance systems can only provide real-time monitoring or manually analyze cases by reviewing the surveillance video. So, it is difficult to use the data sampled from the surveillance video effectively. In this paper, we proposed an efficient video detection object super-resolution with a deep fusion network for public security. Firstly, we designed a super-resolution framework for video detection objects. By fusing object detection algorithms, video keyframe selection algorithms, and super-resolution reconstruction algorithms, we proposed a deep learning-based intelligent video detection object super-resolution (SR) method. Secondly, we designed a regression-based object detection algorithm and a key video frame selection algorithm. The object detection algorithm is used to assist police and security personnel to track suspicious objects in real time. The keyframe selection algorithm can select key information from a large amount of redundant information, which helps to improve the efficiency of video content analysis and reduce labor costs. Finally, we designed an asymmetric depth recursive back-projection network for super-resolution reconstruction. By combining the advantages of the pixel-based super-resolution algorithm and the feature space-based super-resolution algorithm, we improved the resolution and the visual perception clarity of the key objects. Extensive experimental evaluations show the efficiency and effectiveness of our method.

Searching in CCTV: effects of organisation in the multiplex

CCTV plays a prominent role in public security, health and safety. Monitoring large arrays of CCTV camera feeds is a visually and cognitively demanding task. Arranging the scenes by geographical proximity in the surveilled environment has been recommended to reduce this demand, but empirical tests of this method have failed to find any benefit. The present study tests an alternative method for arranging scenes, based on psychological principles from literature on visual search and scene perception: grouping scenes by semantic similarity. Searching for a particular scene in the array—a common task in reactive and proactive surveillance—was faster when scenes were arranged by semantic category. This effect was found only when scenes were separated by gaps for participants who were not made aware that scenes in the multiplex were grouped by semantics (Experiment 1), but irrespective of whether scenes were separated by gaps or not for participants who were made aware of this grouping (Experiment 2). When target frequency varied between scene categories—mirroring unequal distributions of crime over space—the benefit of organising scenes by semantic category was enhanced for scenes in the most frequently searched-for category, without any statistical evidence for a cost when searching for rarely searched-for categories (Experiment 3). The findings extend current understanding of the role of within-scene semantics in visual search, to encompass between-scene semantic relationships. Furthermore, the findings suggest that arranging scenes in the CCTV control room by semantic category is likely to assist operators in finding specific scenes during surveillance.

DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition.

Heterogeneous face recognition (HFR) refers to matching cross-domain faces and plays a crucial role in public security. Nevertheless, HFR is confronted with challenges from large domain discrepancy and insufficient heterogeneous data. In this paper, we formulate HFR as a dual generation problem, and tackle it via a novel dual variational generation (DVG-Face) framework. Specifically, a dual variational generator is elaborately designed to learn the joint distribution of paired heterogeneous images. However, the small-scale paired heterogeneous training data may limit the identity diversity of sampling. In order to break through the limitation, we propose to integrate abundant identity information of large-scale visible data into the joint distribution. Furthermore, a pairwise identity preserving loss is imposed on the generated paired heterogeneous images to ensure their identity consistency. As a consequence, massive new diverse paired heterogeneous images with the same identity can be generated from noises. The identity consistency and identity diversity properties allow us to employ these generated images to train the HFR network via a contrastive learning mechanism, yielding both domain-invariant and discriminative embedding features. Concretely, the generated paired heterogeneous images are regarded as positive pairs, and the images obtained from different samplings are considered as negative pairs. Our method achieves superior performances over state-of-the-art methods on seven challenging databases belonging to five HFR tasks, including NIR-VIS, Sketch-Photo, Profile-Frontal Photo, Thermal-VIS, and ID-Camera.

Motion Balance Ability Detection Based on Video Analysis in Virtual Reality Environment

In recent years, smart camera devices under the Virtual Reality (VR) environment have been widely popularized. These devices can be equipped with fast and effective computer vision applications, including the detection of the balance ability of moving targets. Moving target balance ability detection plays an important role in public security, traffic monitoring and other fields, and is also a basic technology for many vision applications. Therefore, the requirements for accuracy and completeness of detection are getting higher and higher. This article proposes a tracking method Motion Model and Model Updater (MMMU) based on the balance acquisition and model update and intelligent adjustment of the motion model. Improved Motion Model (IMM) is a background sample balance acquisition algorithm based on simple linear iterative clustering, completes the abstraction of background images. Different from other update strategies with a fixed number of frames, the update strategy based on image histogram contrast relies on the human selective forgetting mechanism to better avoid burst frames and process similar frames. Since the data used to detect the balance ability of moving targets is inherently unbalanced, the idea of dealing with imbalance in data mining is introduced into it, and the problem of balance ability detection of moving targets is studied from the perspectives of downsampling and oversampling. In addition, temporal and spatial oversampling of the foreground and selective downsampling of the background are performed to reduce the imbalance of the data set, and the resampled data set is used for modeling and classification. The feasibility of the MMMU algorithm is tested through experiments, and the motion balance ability of the foreground target is detected relatively completely.

Flexible and Thermally Induced Switchable Fire Alarm Fabric Based On Layer-by-Layer Self-Assembled Silver Sheet/Fe3O4 Nanowire Composite.

Textiles with fire detection will appeal for the interior decoration of houses and play a critical role in public security. Herein, we fabricated a sandwichlike fire alarm fabric (Ag@Fe3O4-MS) based on Fe3O4 nanowire (NW) arrays and fish-scale-like silver sheets, designed by in situ layer-by-layer assembly on the surface of polypropylene (PP) nonwoven fabric. The Ag@Fe3O4-MS sensor has fish-scale-like silver sheets as self-assembling electrode layers on the upper and lower sides of fabric, which can be tailored into various shapes and integrated into other flexible electronics. The sensor provides a real-time monitoring strategy for early warning fire detection (below 100 °C). At room temperature, the fabricated Ag@Fe3O4-MS sensor is electrically insulating, while it switches to an electrical conductor when exposed to flame. In view of its fast response time (2 s) and sustained working time (at least 15 min), the sensor with a connected alarm light can immediately alert people of house fires. More importantly, this sensor can provide additional real-time information on the fire location and reliable real-time monitoring of fire rekindling. The sensor was exposed to fire for successive cycles with an average response ofI = 43 mA, confirming the reliable repeatability to detect fires. This ultralight, flexible Ag@Fe3O4-MS sensor could have broad applications in home safety. Moreover, the sandwichlike design provides a reliable strategy to modify household fabric items to provide a fire warning function.

How to Assess the Quality of Compressed Surveillance Videos Using Face Recognition

Video surveillance plays an important role in public security. To store the growing volume of surveillance videos, video compression is beneficial for reducing video volume; however, it is simultaneously harmful to the video quality. Video quality assessment (VQA) methods help to achieve a tradeoff between the data volume and perceptual quality of compressed surveillance videos. Generally speaking, surveillance video quality assessment (SVQA) is different from conventional VQA, because surveillance videos are usually used for specific tasks, e.g., pedestrian recognition, rather than for entertainment purposes. Therefore, in this paper, we propose two full-reference SVQA methods based on the concept of quality of recognition. We first design two new tasks, distorted face verification (DFV) and distorted face identification (DFI), based on which we further propose two SVQA methods, DFV-SVQA and DFI-SVQA, and corresponding quality metrics. The core components of the DFV-SVQA and DFI-SVQA methods are feature extractors (a DFV model and a DFI model), which we construct using convolutional-neural-network-based face recognition models. In addition, we construct a real-world surveillance video data set, based on which we analyze how various factors, including the video codec, compression level, face resolution, and light intensity, affect the quality of compressed surveillance videos. We find that, compared with conventional VQA methods, our methods are more effective in measuring the quality of surveillance videos while maintaining an acceptable time efficiency.

Robust Feature-Based Automated Multi Viewhuman Action Recognition System Using Machine Learning

Automated human action Recognition has the potential to play an important role in Public security. In this project it compares three practical, reliable and generics systems for multiview video based human action recognition namely the nearest classifier, Gaussian mixture model classifier and nearest mean classifier.

Remote Sensing Target Tracking in UAV Aerial Video Based on Saliency Enhanced MDnet

Remote sensing target tracking in the aerial video from unmanned aerial vehicles (UAV) plays a key role in public security. As the UAV aerial video has rapid changes in scale and perspective, few pixels in the target region, and multiple similar disruptors, and the main tracking methods in this research field generally have relatively low tracking performance and timeliness, we propose a remote sensing target tracking method for the UAV aerial video based on a saliency enhanced multi-domain convolutional neural network (SEMD). First, in the pre-training stage, we combine the least squares generative adversarial networks (LSGANs) with a multi-orientation Gaussian Pyramid to augment typical easily confused negative samples for enhancing the capacity to distinguish between targets and the background. Then, a saliency module was integrated into our tracking network architecture to boost the saliency of the feature map, which can improve the representation power of a rapid dynamic change target. Finally, in the stage for generating tracking samples, we implemented a local weight allocation model to screen for hard negative samples. This approach can not only improve the stability in tracking but also boost efficiency. The comprehensive evaluations of public and homemade hard datasets demonstrate that the proposed method can achieve high accuracy and efficiency results compared with state-of-the-art methods.

Ratiometric Luminescent Sensor of Picric Acid Based on the Dual-Emission Mixed-Lanthanide Coordination Polymer.

Powerful explosive sensors play a key role in public security and environmental protection. Herein, we report a series of isostructural lanthanide coordination polymers [Ln2L1.5(NMP)2] n (LnL: Ln = Eu, Gd, Tb, Dy, Ho, and Er; H4L = [1,1':4',1″-terphenyl]-2',4,4″,5'-tetracarboxylic acid; NMP = N-methyl-2-pyrrolidone) and mixed-Ln LnL (Eu xTb1- xL, Eu xGd1- xL, Tb xGd1- xL, and Eu xTb0.02- xGd0.98L). Luminescence studies show that both H4L and GdL emit strong fluorescence and phosphorescence at 77 K while only fluorescence at room temperature, and TbL exhibits strong Tb3+ characteristic emission, although the energy difference between the triplet excited state of H4L (20661 cm-1) and the 5D4 energy level of Tb3+ (20500 cm-1) is very small. By doping Eu3+ and Tb3+ into GdL, we obtained Eu xTb0.02- xGd0.98L emitting warm white light. For TbL and Tb0.01Gd0.99L showing dual emission, upon addition of picric acid (PA) into their suspensions in Tris-HCl buffer, Tb3+ emission decreases slowly; however, the ligand-based emission is sharply quenched, rendering TbL and Tb0.01Gd0.99L excellent single-lanthanide and mixed-lanthanide ratiometric luminescence PA sensor materials, respectively.

Robust Feature-Based Automated Multi-View Human Action Recognition System

Automated human action recognition has the potential to play an important role in public security, for example, in relation to the multiview surveillance videos taken in public places, such as train stations or airports. This paper compares three practical, reliable, and generic systems for multiview video-based human action recognition, namely, the nearest neighbor classifier, Gaussian mixture model classifier, and the nearest mean classifier. To describe the different actions performed in different views, view-invariant features are proposed to address multiview action recognition. These features are obtained by extracting the holistic features from different temporal scales which are modeled as points of interest which represent the global spatial-temporal distribution. Experiments and cross-data testing are conducted on the KTH, WEIZMANN, and MuHAVi datasets. The system does not need to be retrained when scenarios are changed which means the trained database can be applied in a wide variety of environments, such as view angle or background changes. The experiment results show that the proposed approach outperforms the existing methods on the KTH and WEIZMANN datasets.

Adaptive representation-based face sketch-photo synthesis

Face sketch synthesis plays an important role in public security and digital entertainment. Most existing face sketch synthesis methods employ pixel intensities or other features to synthesize the whole image. Since different regions have their distinctive properties, they should be represented by different features. This paper presents a novel adaptive representation-based face sketch synthesis method that different regions are represented with different features. It combines multiple features generated from face images pass through several filters and deploys Markov networks to exploit the interacting relationships between neighboring image patches. The proposed model is optimized using an alternative optimization strategy. Experimental results on the Chinese University of Hong Kong (CUHK) face sketch database (CUFS) demonstrate the effectiveness of the proposed method in comparison to state-of-the-art methods.

A Markovian analytical framework for public‐safety video sharing by device‐to‐device communications

SummaryMonitoring video of city surveillance camera plays an important role in public security and disaster relief, which is often used by first responder teams, such as firefighters and police officers. The first responders in emergency situations require consistent connection with one another and request the remote real‐time monitoring video for effective cooperation and coordination. However, the capacity and privacy of public wireless networks fail to satisfy the requirements in many emergency scenarios, which often leads to exceptionally high traffic loads and insecurity. Device‐to‐device (D2D) communications have been deemed a key solution for this problem, as responders can use D2D links for traffic offloading and secure communications. To investigate the D2D‐based solution for public safety video sharing, this paper focuses on the Focus Geographical Area (FGA) video consisting of multiple camera streams requiring higher bandwidth consumption than that of the traditional single‐camera stream, which attracts a large number of contents delivery requests in emergency situations. This paper develops a new Traffic Burden Switching Markovian (TBSM) model to evaluate the performance of transmitting real‐time FGA video in wireless networks with D2D communications. First, a novel D2D area model is introduced to characterize link‐switching in wireless D2D communication networks. Based on the proposed D2D area model, the state and transition matrix of TBSM model are then derived by jointly considering user mobility, link‐switching, and FGA video view‐switching. Thereafter, some key performance metrics including system offloading ratio, D2D link‐switching ratio, and view‐switching ratio are derived on the basis of coverage probability and ergodic rate. The performance results show the significant varying performance among D2D areas with different geographical locations in D2D enabled wireless networks, which is referred to as multi‐D2D‐area diversity. The excellent match between simulation and model results validates the accuracy of the TBSM model, which can be used to provide guidelines for the deployment and optimization of future wireless video networks with D2D communications.

Configuration, Deployment, and Scheduling Models for Management and Optimization of Patrol Services

This paper presents a decision support system (DSS) and its models for patrol service center (PSC). PSC plays an important role in public security and emergency management. The configuration, deployment, and scheduling of resources of PSC are important for improving the efficiency of patrol-related resources, service quantity, and emergency response capability. A series of decision-making models of the DSS are studied. First, the criteria and models are proposed for configuring and deploying PSCs; second, three types of models for incremental, direct, and redeployment optimization are built in views for decisions aiming at PSC configuration, deployment, and scheduling problems; third, considering three typical patrol-related service scenarios (alarm assignment, main road blockade, and besiege program), three scheduling models are built, respectively, for PSC-related service and coordination of multiple PSCs. This work contributes to the literature on patrol services and network optimization problems in the following aspects: based on a series of models, a DSS framework is designed for PSCs; the models are formulated for resource management and scheduling upon geography information system; coordination strategies among close PSCs are incorporated into decision models. These features are examined in integration manners. The assessment criteria and optimization models studied in the paper are beneficial for building DSSs for PSC.

Application of GIS for the determination of hazard hotspots after direct transportation linkages between Taiwan and China

A robust and reliable risk assessment procedure for the determination of transportation hazards between Taiwan and China needs to be developed. These hazards, which are related to mixed types of transport, including both air and maritime routes, play an important role in public security, and their effects need to be taken into consideration. After the implementation of direct cross-strait transportation, it has become necessary to master the spatial characteristics and distributions of maritime space policing cases and to plan effective control strategies in order to maintain public security. This study employs the Geographic Information System (GIS) technology to analyze the spatial locations of maritime cases that arose in the Taichung Harbor area from 2000 to 2010. The characteristics related to the spatial characteristics are surveyed, estimates are made utilizing the gathering and scattering model, hotspots are explored, and planned patrol and monitoring points drafted to provide assignment information for coastal patrol institutions.