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Vehicle security is one of the major concerns that the entire world is currently experiencing. People generally own automobiles, yet these automobiles are not always secure. Vehicle theft occurs in parking lots, public places, and other unsafe areas. The vehicle’s manufacturer does not consider the vehicle security system to be a factor in the overall cost of the vehicle. Nowadays, only a few vehicles come equipped with high-priced security systems. Door locking, alarm system, GSM, GPS, and other security features are built into high-end vehicles only. There is a necessity to build a low-cost security system for vehicles that common people can afford it and the manufacture can built-in the security system in a wide range of automobiles. This paper proposed a method for vehicle theft detection, tracking, and accident identification using the Internet of Things.

Nowadays, automobile thefts and the production of vehicle are increasing in yearly throughout the world. So, vehicle theft is a universal problem. Yearly around 10 million property and automobile cases are reporting. There are so many recent technologies evolving and new methods are being upgraded to overcome this issue. The methods involved in vehicle theft detection have become aware to everyone including the burglars and they try to break the system and steal the vehicle.It is important to keep our properties secure. Ofcourse as the technology increases the thieves are looking for better plans.Prevention of accidents is better than facing them later. Today we have many approaches to prevent the accidents. prevention is nothing but allowing people to do things under limit. This paper work presents an model which can solve the addressed problem.

Now-a-days, vehicle thefts have very high impact on public safety. There is a need in preventing those thefts. For vehicle theft detection and tracking system, we design a device which is cheap, compatible and also efficient. This device is vehicle tracking hardware which consists of GPS and GSM module for tracking the location of vehicle using co-ordinates and also it has a camera for live face capture of the intruder. In our system, whenever the vehicle’s engine starts, an OTP will be sent to the owner’s mobile for verification, if the OTP is not verified or incorrect OTP is entered, buzzer starts and an alert intimating theft will be sent to the owner. This hardware device is installed on the vehicle in the place where the intruder finds it difficult to identify and remove. The proposed system is an updated version of existing locking system in such a way that the device alerts the owner and notifies with updated location of vehicle at regular intervals and image of the intruder can be seen using live face capture system which secures the vehicles from theft.

PurposeThis paper aims to explore the relationship between adjustments in parking regulations in major American cities in response to the 2019 novel coronavirus and vehicle thefts.Design/methodology/approachVehicle theft data from the 25 most populous American cities were collected between 2019 and 2020. Information was also collected on the type, severity and number of changes to parking enforcement regulations made in March and April 2020 in response to the outbreak of the coronavirus in the USA.FindingsThis analysis shows that the relaxation of parking regulations is associated with a significant increase in the number of vehicle thefts in major metropolitan areas.Research limitations/implicationsAlthough this research cannot prove a cause and effect relationship, statistical analysis of the data shows that policy change and vehicle thefts are related variables.Practical implicationsGuide for future policies-increase awareness; promote safety; promote accountability.Originality/valueThe comparison of increased vehicle thefts to parking enforcement policies; to policymakers, the general public.

Nowadays, vehicle theft has been increased drastically all over the world. The prevention of vehicle theft is essential to enhance vehicle security. A large number of vehicles have been stolen for the lack of essential foundation and administration of a secure platform. Several existing systems were developed to protect the vehicle from unauthorized access. But, they suffer from various limitations such as data security, the possibility of cybercriminals activities, leakage of personal information, and centralized System. We propose a vehicle theft detection framework using a decentralized and secure platform to increase the security level of the vehicle. Blockchain and smart contracts are used to provide the security of the stored data on the ledger and authenticate a genuine user automatically with greater accuracy. In this paper, we present 2-Step Authentication (2SA) and unauthorized access detection algorithms. The 2SA ensures the secure accessibility of the application by providing the randomly token chosen by the user. The proposed framework can provide vehicle security and owner’s privacy. In this proposed framework, more than one person can drive the vehicle authorizing by the vehicle owner without hampering stored data in the vehicle device.

Frequent vehicle thefts have a highly detrimental impact on public safety. Thanks to surveillance equipment distributed throughout a city, a large number of videos that can be used to recognize vehicle theft are available. However, vehicle theft behavior has the characteristics of a small criminal target and small movement. Hence, the existing action recognition algorithms cannot be directly applied for the recognition of vehicle theft. In this paper, we propose a method for vehicle theft recognition based on a spatiotemporal attention mechanism. First, a database of vehicle theft is established by collecting videos from the Internet and an existing dataset. Then, we establish a vehicle theft recognition network and introduce a spatiotemporal attention mechanism for application when extracting the spatiotemporal features of theft. Through the learning of adaptive feature weights, the features that contribute most greatly to recognition are emphasized. Simulation experiments show that our proposed algorithm can achieve 97.04% accuracy on the collected vehicle theft database.

The article summarizes empirical data and characterizes regional criminological features of car thefts on the basis of the conducted research. According to the Authors, the specifics of the method of acquisition and subsequent sale of !he car ispredetermined by its brand and value. In this regard, it isproposed lo classify the cars according to their price category: expensive, medium, and low cost. Proceeding from this criterion, actions on preparation for a crime, a place and time of Commission of thefts of cars, ways of their realization are considered. On the basis ofjudicial practice , the method.1· of committing crimes by a group

The need for a security alarm system for the vehicles begin at the end of the 19th century when increased vehicles production began. The first security systems for vehicles were much simpler than security alarm systems for vehicles today. The truth is that the vehicle thieves are more resourceful so the simple and known security alarm systems for the vehicles does not have a purpose anymore. The development of technology has modernized and perfected the security alarm systems for vehicles. The security alarm systems for vehicles now enables two-way communication between the vehicle owner and the security alarm system inside the vehicle. However, despite progress in this field, vehicles theft is still one of the most common. In the past year, approximately 775 cases of theft of expensive vehicles have been documented in Bosnia and Herzegovina. Worldwide, according to the Insurance Information Institute, over a million vehicles are stolen annually in the USA, corresponding to vehicle theft every 23 s. In today’s time there is a need to develop a high technology quality security alarm system for a vehicles that will concur with the requirements of today’s technology in terms of performance and functionality in the conditions of vehicle theft. In this paper is a proposed the smart security alarm system for vehicle that can fulfill all the requirements. The proposed system is realized using GSM/GPRS technology for two-way communication for system-user, and the GPS technology for locating the vehicle.

Vehicle theft in East Java is a crime that causes unrest and anxiety in the public. The increased rate of vehicle theft is due to several factors, including economic, environment, low education, increased unemployment, and lacking legal awareness. One way to anticipate an increased level of theft is by predicting the possibility of theft based on previous incident data. This research is focused on the design of an intelligent system to predict the locations prone to vehicle theft using Backpropagation Neural Network. The training dataset used was obtained from data on the location of vehicle thefts in the East Java Regional Police from 2015 to 2019. Backpropagation architecture model 7-10-6 was used. The best level of accuracy for the performance of this model is 100%, with an epoch of 65 iterations, cross entropy of 5.188, and an error value of 0. Prediction test was carried out on ten new theft data in 2020 and 2021. The test resulted in a quite accurate level of accuracy in predicting locations prone to theft in a short time and gave correct prediction results.

Analysis of STR profiles obtained from touch DNA has been very useful to the elucidation of crimes. Extraction method may be determinant for the recovery of genetic material collected from different surfaces. Vehicle theft is one of the most common crimes in Sao Paulo city, Brazil, but collection of biological traces in car steering wheels is not considered, because of the belief that profiles generated won’t be able to identify the thief, only the owner. This study aimed to analyze the efficacy of extraction methods for obtaining DNA profiles in samples collected from steering wheels. Eight criminal acts were simulated with 2 different individuals each (mixture of victim and thief), in duplicate, in order to compare two extraction methods: DNA IQ™ and Casework Direct Kit (both Promega Corporation). Genetic material was collected by double swab method and quantified by Quantifiler™Trio (ThermoFisher Scientific). Amplification was conducted with PowerPlex® Fusion System (Promega). It was possible to obtain STR profiles for all experiments. The mixtures were compared with reference profiles to evaluated how many alleles of each donor were observed. Samples extracted with Casework Direct Kit obtained STR profiles with higher averages of alleles for primary and secondary donors (88.7% and 59.9%, respectively) than those extracted with DNA IQ™ (60.4% and 38.1%, respectively). This could be explained by the differences established in the protocols of both methods, since DNA IQ™ is based on successive washes and can result in loss of DNA, whereas Casework Direct Kit minimizes this problem. We concluded that Casework Direct Kit was more efficient for processing touch DNA samples than DNA IQ™.

Automated valet parking, deemed as a key milestone on the way to autonomous driving, has great potential to improve the “last-mile” driving experience for users. On the other hand, it triggers serious risks on vehicle theft and location privacy leakage. To address these issues, we propose a secure and privacy-preserving automated valet parking protocol for self-driving vehicles. The proposed protocol is characterized by extending anonymous authentication to support two-factor authentication with mutual traceability for reducing the risks of vehicle theft and preventing the privacy leakage of users in automated valet parking. Specifically, based on one-time password and secure mobile devices, two-factor authentication is achieved between vehicles and smartphones to ensure vehicle security in remote pickup. By exploiting the BBS+ signature and the Cuckoo filter, user location privacy is protected against the curious parking lots and service providers. In addition, the traceable tags are designed to enable a trusted authority to identify the vehicles and users for localizing a stolen or missing vehicle and preventing the slandering of greedy users. Finally, formal security analysis on the proposed protocol is given to show that the authentication, anonymity, and traceability can be reduced to standard hard assumptions, and performance evaluation demonstrates the proposed protocol is efficient and practical to be implemented in autonomous driving era.

The article discusses motor vehicle theft and legalisation. Despite modern methods of securing this kind of property, such offences are still committed by criminals. This type of crime is determined by the large and fast fi nancial profi ts, which criminals make not only from selling such cars, but also from ransoms obtained from the owners for returning the stolen vehicles. Criminal groups are also interested in vehicles carrying cargoes that are sold by the group or become objects of ransom obtained from owners. In the article, the author presents methods and techniques of stealing vehicles used by thieves in recent times. Moreover, the structure of criminal groups and the individual roles of their members are discussed. Furthermore, methods of legalising vehicles through previously stolen or counterfeited documents are presented, thanks to which buyers and public authorities are not able to get an idea of the practice. In most cases, the sale of properly prepared vehicles and documents is carried out under the pressure of time and attractive prices. In the last part of the article, the author presents the issue of obtaining compensation for stolen vehicles. This type of criminal activity is also a source of income for groups dealing with car theft.

Several cities around the world face security problems, such as vehicle theft. Locate and recover these vehicles are challengers for authorities. In smart cities, citizens can collaborate with authorities by sensing urban and environmental data, so-called crowdsensing. This work introduces Crowd-Auto, a crowdsensing approach that utilizes a crowded camera network from houses and commerce to identify vehicle plates, query on official databases and inform the authorities when stolen vehicles are identified. We've developed a prototype and demonstrated that Crowd-Auto is viable for allowing citizens to cooperate and improve security in cities.

Vehicle theft represents one of the most frequent crimes in Chile and in the world. In this work, we propose an application of the GCLSTM (Graph-Convolutional Long Short Term Memory) neural network that combines a graph convolutional model with a LSTM for the prediction of vehicle thefts in the metropolitan region of Chile. The graph architecture considers the characteristics found in the neighbors to an area, assuming that the thefts of vehicles in nearby municipalities have similar patterns. For implementing the GCLSTM, first a smoothing technique based on LOESS regression was used for denoising the number of theft events for day, then the smoothed series of the last 30 days was considered as the input of the GCLSTM neural network for predicting the number of thefts in the following day. The results provided a better performance of the GCLSTM compared to a traditional LSTM, achieving an R2 of 0.86.

The COVID-19 pandemic had a substantial impact on the historical criminal trend around the world. This study explores the early impact of COVID-19 lockdown on selected crimes in Dhaka, Bangladesh. Based on open data of the total number of arrests reported by Dhaka Metropolitan Police (DMP), an uninterrupted historical time series analysis is applied to evaluate the immediate impact during and after the official stay-at-home order due to COVID-19. Auto-regressive moving average (ARIMA) modeling technique was used to compute 6-month-ahead forecasts of the expected frequency of the total number of arrests for illegal arms dealing, vehicle theft, and narcotics trafficking in the absence of the pandemic. These forecasts were compared with the observed data from April 2020 to September 2020. The results suggest that the observed numbers of total arrests for vehicle thefts and illegal arms dealing are not significantly different from their predicted values. However, the observed frequency of the total number of arrests for illegal drug trafficking shows a steep upward trend, which is 75% more than that of the expected frequencies. Estimated results are used to recognize scopes and suggestions for future research on the relationship between crimes and the pandemic.

Violence is a word that encompasses several meanings ranging from an actual fight to theft and several types of harassment. Therefore, violence detection through surveillance systems can be a quite difficult yet important task. The increasing use of carpooling services and vehicle sharing brought the need to implement a sufficient general surveillance system for monitoring these vehicles for assuring the passengers’ safety during the ride. This paper raised the literature for this matter, finding fewer research papers than it was expected for the in-vehicle perspective, noticeably to sexual harassment. Most of the research papers focused on out-vehicle issues such as runs over and vehicle theft. In-vehicle electronic components security and cockpit user experience were perceived as major concern areas. This paper discusses these findings and presents some insights about in-vehicle surveillance., This work is supported by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project no 039334; Funding Reference: POCI-01-0247-FEDER-039334]., This work has been supported by national funds through FCT – Fundação para a Ciência e Tecnologia through project UIDB/04728/2020.

It is a foregone conclusion that property crimes will hit ten million. Among such, within the steal list, the vehicle is flat-top and sometimes controls entire components of the earth. There have been some recent technical advances and new strategies are being developed to overcome this downside. The ways involved in the detection of vehicle theft are noted for interference by some or everything, as well as shields. This research helps to prevent the vehicle stealing from third parties with the help of RFID card and authorized key. Here, RFID reader connected to the car and if anyone enter into car and they requires the card authorization. Keypads were connected to the engine and the authorized person can only enter into the car by pressing passwords. If the password is incorrect, buzzer will produce noise continuously. The proposed system gives the regular updates to the car owners regarding the location of car by using GPS technology.