Top PDF Detection of Anomalous Vehicle Loading

Detection of Anomalous Vehicle Loading

Detection of Anomalous Vehicle Loading

regardless of its orientation relative to the imaging system (as long as the entire target is visible and well resolved). This can be done by identifying the coordinates on the edges of the target in the image coordinate system, mapping these coordinates to rays in the world coordinate system, and solving for the position and orientation for which the target’s true dimensions match. This means that the three-dimensional position coordinates of a target with known dimensions can be measured as a function of time using a calibrated video camera system positioned in an off- normal orientation relative to the target. Garibotto et al. developed a method for measuring the velocity of a vehicle by exploiting the known spacing between characters on a license plate and were able to measure the speed of a vehicle with accuracy better than 4 % [13]. However, for a measurement of this type to be an accurate method of measuring vehicle position, the imaging system must be positioned with an unobstructed view of the target and the dimensions of target must be known. For an unobstructed view of the license plate, the video camera system would need to be positioned so as to image the front or the rear of the vehicle, requiring the video camera system to be in the path of the vehicle, or above it. A more desirable position for the video camera system from an operational standpoint would be on the side of the road. With a camera looking across a road, perpendicular to the flow of traffic, the motion of the vehicle would be captured largely as translational motion in consecutive frames of a video as opposed to a head-on camera view, in which vehicle motion is captured as changes in object depth. As a result, a cross-road method could utilize more of the camera’s field-of-view then a head-on method, which should result in better accuracy. In order to move to the cross-traffic mounted camera system, we need to relax the restriction of knowing our target’s dimensions. To do this we can move from a single camera system to a two-camera system.
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An Approach For Vehicle Detection In Complex Environments

An Approach For Vehicle Detection In Complex Environments

[9]. In addition, many approaches have been proposed for tackling related problems in ITS. The model based approach [10] uses a 3-D model to detect vehicles. In this method, different models that correspond to different types of vehicles are created. Song et al. [11] and Koller et al. [12] used an active contour method to track vehicles. In this method, the vehicles could easily be tracked, and computation loading could significantly be reduced. However, system initialization was a critical risk. Coifman [13] developed a vision-based system with gradient operator to detect subcorner features of the vehicles and grouped these features to detect the vehicles. The advantage of this method was that it was less sensitive to change in illumination. On the other hand, this method could meet the challenge of determining grouping conditions. Wang et al. [14], detected the motion information of the spatial– temporal wavelet of video sequence. Cucchiara et al. [15] integrated moving edge detection and headlight detection into a hybrid system. This system worked not only during the day but also at night. Unlike most methods referring to background image, they used a three-image difference to detect moving edge. This method reduced both the dependence on background and the time of background learning. However, noise affected the system to a great extent. Background segmentation was one approach for extracting the common part between different images in a frame. With good flows of learning and updating, objects could more completely be extracted. Beymer et al. [16] proposed a vehicle-tracking algorithm to estimate traffic parameters using corner features. In addition, Liao et al. [17] used entropy as an underlying measurement to calculate traffic flows and vehicle speeds. Baker et al. [18] proposed a 3-D model matching scheme to classify vehicles into various types, such as wagons, sedan, and hatchback. Furthermore, Gupte et al. [19] proposed a region-based approach to track and classify vehicles based on the establishment of correspondences between regions and vehicles. In [16], [19], and [20], a manual method of camera calibration has been presented to identify lane locations and corresponding lane widths.
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Accident Detection System Independent of In-Vehicle Detection System

Accident Detection System Independent of In-Vehicle Detection System

A complete system has been developed for the task of emergency detection and tracking. In order to provide more effective application services, we use the concept of fat- client to install the built-in module on the client side including GPS receiver connection module, analysis module, loading map module, driving management subsystem and emergency accident deal subsystem. This application makes the system respond to users quickly while transforming coordinates or loading map.

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Model for Strengthening Accuracy through Detection of Anomalous Firewall Policy Rules

Model for Strengthening Accuracy through Detection of Anomalous Firewall Policy Rules

In this paper, we have presented a model for detection the anomalies firewall policy rules, the model plays an important role in detection the anomalies firewall policy rules in network security, the main factor is the accuracy of detection. Moreover, the accuracy factor is very sensitive and important in detection operation; the model reduce the mismanagement through network administrator and minimize time and effort in finding and filtering the anomalies, the model help the administrators in managing and configuration. Detected in early about the anomalies before add any new rules in firewall policy rules. In future work we will design and create model for detection the anomalies firewall policy rules in network security have IP6.
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Anomalous Payload-based Worm Detection and Signature Generation 1

Anomalous Payload-based Worm Detection and Signature Generation 1

The core mindset of most security architectures dictates that each site or domain is an enclave, and any external site is regarded as the enemy. Worm writers and attackers, on the other hand, do collaborate and share information amongst themselves about vulnerabilities and tools to rapidly create new attack exploits, launch them, and form shared drone sites, often simultaneously worldwide. Defenders still depend on centralized management to update detection signatures and deploy patches on time scales that are no longer tenable. We posit that a collaborative security system [17, 18], a distributed detection system that automatically shares information in real-time about anomalous behavior experienced at the moment of attack among collaborating sites, will substantially improve protection against wide-scale infections. Indeed, most collaborating systems can be protected against new exploits by limiting propagations to a small set of initial victims. By integrating the PAYL anomalous payload sensor into a collaborative security system, and exchanging information about suspect packet content, the resulting system not only can detect new zero-day exploits but can also automatically generate new zero-day attack signatures on-site for content filtering. In this paper, we demonstrate this strategy and show that a collaborative detection system using multiple PAYL sensors, each trained on a distinct site, can accurately detect an emerging worm outbreak very fast, and reduce the incidence of false positives to nearly zero.
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Vehicle Detection And Counting From Unmanned Aerial Vehicle (UAV)

Vehicle Detection And Counting From Unmanned Aerial Vehicle (UAV)

Besides that, vehicle theft cases are rapidly increasing. Therefore, we need a system which can help us to detect stolen vehicles so finding back stolen can be carried out more effective. Therefore we need a more up-to-date approach to detect vehicles on the earth surface so that searching tasks can be done immediately and effectively.

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A Blockchain-Based Anomalous Detection System for Internet of Things Devices

A Blockchain-Based Anomalous Detection System for Internet of Things Devices

Internet of Things devices are highly susceptible to attack, and owners often fail to realize they have been compromised. This thesis describes an anomalous-based intrusion detection system that operates directly on Internet of Things devices utilizing a custom-built Blockchain. In this approach, an agent on each node compares the node's behavior to that of its peers, generating an alert if they are behaving differently. An experiment is conducted to determine the effectiveness at detecting malware. Three different code samples simulating common malware are deployed against a testbed of 12 Raspberry Pi devices. Increasing numbers are infected until two-thirds of the network is compromised, and the detection rate is recorded for each trial. The detection system is effective, catching at least one malicious node in every trial with an average of 82% detection. This research presents an effective,
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Data Stream Subspace Clustering for Anomalous Network Packet Detection

Data Stream Subspace Clustering for Anomalous Network Packet Detection

The anomaly detection algorithm proposed in this study was tested with packets of three attack types. To simulate attacks to a network, 66 different types of Ge- neric HTTP attacks were included in our study. These HTTP attacks included threats caused by standard attacks like buffer overflow, URL decoding error and input validation error. Shellcode attacks were also included as they are a special type of packet where the payload con- tains executable code. CLET attacks attempt to hide from the detection algorithm by polymorphically enciphering the payload of the packet to appear normal. These attacks were also extracted from the packet’s payload using Jpcap and inserted into the normal packet stream. On each day of the DARPA dataset, the first 20% of the packets were set aside for a parameter-tuning phase, and the remaining 80% for a full-scale anomaly detection testing phase.
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Anomalous Network Packet Detection Using Data Stream Mining

Anomalous Network Packet Detection Using Data Stream Mining

The anomalous data used in this study were compiled by [6], and are freely available online (http://roberto. perdisci.com/projects/mcpad). We chose to analyze the algorithms’ performance in the detection of three out of the four attack types provided: Generic HTTP Attacks, Shell-code Attacks, and CLET Shell-code attacks. [6] obtained 63 of the attacks included in their Generic HTTP dataset from [13]. These attacks include a variety of HTTP attacks collected in a live environment from test web servers, as well as various archives and data- bases. The attacks fall into several categories, including buffer overflow, URL decoding error, and input valida- tion error, and were directed against numerous web serv- ers such as Microsoft IIS, Apache, Active Perl ISAPI, CERN 3.0A, etc. [6] further supplements these attacks, bolstering the dataset to include a total of 66 HTTP threats. The Shell-code attack dataset includes 11 shell-
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Vehicle and Pedestrian Detection Applications

Vehicle and Pedestrian Detection Applications

an attention mechanism based on inward motion, textures, horizontal edges, vertical symmetry and entropy. The detected objects are passed on to a SVMbased classifier. After classification, detected objects are tracked using Kalman filtering. A large database containing thousands of examples extracted from real images has been created for learning purposes. After assessment of the practical results achieved in our experiments, the following general conclusions can be summarized: • Car dynamics (yaw rate and velocity) have to be taken into account in order to improve data association and tracking stages. Moreover, at present, the region of interest is statically fixed. By using yaw rate and car velocity variables we can define a more precise region of interest and evaluate the risk for each candidate. • The performance of the vehicle detection module is significantly increased by building on the output provided by the LDWS function. • The presence of large shadows on the asphalt due to vehicles circulating along the road produces negative effects on the candidate selection mechanism, yielding to inaccuracy in measuring the distance to the vehicles.
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Geostatistical Studies and Anomalous Elements Detection, Bardaskan Area, Iran

Geostatistical Studies and Anomalous Elements Detection, Bardaskan Area, Iran

The aim of this study is geostatistical analysis and detection of anomalous elements in the Bardaskan area (in geological map of Bardaskan on scale 1:100,000 which is provided by the GSI organization). The study area is lo- cated in Khorasan province of Iran. Due to the availability of lithogeochemi- cal regular data in the region as well as the importance of exploration of metal minerals in order to simplify and summarize the geochemical map, geostatis- tical methods were used to identify the mineralization potential of the region. Initially, using single-variable and multivariate statistical methods, anomalous elements were separated. Then, the thresholds (various communities) for the titanium element that was most likely to be anomalous were identified. Using these limits, the discriminant analysis was applied to the elements. Titanium, iron and magnesium elements were identified as the main mineral elements in the region. These elements indicate mineralization in the mafic bed rocks. Finally the map of the concentration of titanium element was mapped across the region with Kriging interpolation method. As a result, two anomalies of the titanium element in the region were identified.
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ANOMALOUS PAYLOAD-BASED WORM DETECTION AND SIGNATURE GENERATION USING KIDS

ANOMALOUS PAYLOAD-BASED WORM DETECTION AND SIGNATURE GENERATION USING KIDS

anomaly detection (or, more generally, keyed and further discussing what realistic should be used to assess their Perhaps the first obvious question is whether it makes oduce some secret material into a algorithm so as to make evasion harder. To the knowledge, all the approaches explored so evasion fall into one of the two strategies discussed in Section 2, namely randomizing ssification process (e.g., [3], [20]) or optimally sensitive perspective (e.g., [5]). Anagram [22] is a special case, since it explicitly possesses the notion of a “key” (bitmasks used to choose what parts of the payload will be analyzed). we are not aware of any work studying strength of Anagram against key-recovery attacks. All in all, we believe that the idea of learning a with a key is worth exploring. However, we suggest fundamental properties that any such keyed scheme 1. The designers must prove, or at least give sound heuristic arguments, that evasion is computationally infeasible1 without knowing the key.
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Vehicle and Pedestrian Detection in eSafety Applications

Vehicle and Pedestrian Detection in eSafety Applications

horizontal lines represents a possible vehicle candidate, its width is compared to that of an ideal car. The ideal car width is obtained for each vertical coordinate using the camera pinhole model explained in section 2.2. Once the car width is computed at the current frame it is compared to the collection of horizontal lines found af- ter the thresholding analysis. If they are similar to some extent defined by an empirical value, a square area above the collection of horizontal lines, denoted as candidate ROI, is considered for further analysis. The aim is to compute the entropy of the candidate ROI and its ver- tical symmetry. Only those regions containing enough entropy and symmetry are identified as potential vehi- cles. Figure 10 shows a detailed block diagram of the detection procedure and figure 11(a) depicts an example of the detection step.
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Vehicle Accident Detection And Prevention System

Vehicle Accident Detection And Prevention System

ABSTRACT : Accident prevention and detection system is used for reducing the accidents rate and its after effects providing help as soon as possible .The sensors like eye blink sensor, alcohol sensor, obstacle sensor, vibration sensor and temperature sensor are programmed to work in coordination with the engine ignition system, buzzer to avoid any accident that can occur due to drowsiness, drunk driving or over revving of engine. It uses a smart helmet that has wireless control to the ignition system .The project also concentrates on detecting an accident with the help of vibration sensor and works in coordination with GPS and GSM system to intimate the accident to preset and variable receivers and get the rescue team to the spot to provide required support.
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Alcohol Detection and Accident Prevention of Vehicle

Alcohol Detection and Accident Prevention of Vehicle

A safe driving system of vehicle for drunk and driving cases, In this project we have used an alcohol detecting sensor in vehicle which senses and detects alcohol gases and sends messages continuously to their relatives within every 5 minutes. In this process arm7 microcontroller is connected with GSM and GPS modules. GPS module gets the position of vehicle with longitude and latitude then via GSM it sends the messages to the relative of the driver until he reaches home safely. We have also used car accident prevention technology with ultrasonic sensor which also sends messages via GSM to relatives of the driver while accident happens of vehicle.
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A Vision based Vehicle Detection System

A Vision based Vehicle Detection System

In recent years, automotive manufacturers have equipped their vehicles with innovative Advanced Driver Assistance Systems (ADAS) to ease driving and avoid dangerous situations, such as unintended lane departures or collisions with other road users, like vehicles and pedestrians. To this end, ADAS at the cutting edge are equipped with cameras to sense the vehicle surrounding. This research work investigates the techniques for monocular vision based vehicle detection. A system that can robustly detect and track vehicles in images. The system consists of three major modules: shape analysis based on Histogram of oriented gradient (HOG) is used as the main feature descriptor, a machine learning part based on support vector machine (SVM) for vehicle verification, lastly a technique is applied for texture analysis by applying the concept of gray level co-occurrence matrix (GLCM). More specifically, we are interested in detection of cars from different camera viewpoints, diverse lightning conditions majorly images in sunlight, night, rain, normal day light, low light and further handling the occlusion. The images has been pre-processed at the first step to get the optimum results in all the conditions. Experiments have been conducted on large numbers of car images with different angles. For car images the classifier contains 4 classes of images with the combination of positive and negative images, the test and train segments. Due to length of long feature vector we have deduced it using different cell sizes for more accuracy and efficiency. Results will be presented and future work will be discussed.
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Development of a noisy vehicle detection system

Development of a noisy vehicle detection system

The ADR requires that tested vehicles be operated full throttle in the measurement region thus producing a maximum noise emission. Vehicle operation cannot be controlled on the roadway however it is likely that most cars will not be operating full throttle and will therefore produce a sound level lower than that if measured according to Australian Standards. This suggests that if the recorded levels of a normally operated vehicle exceed the prescribed limits, the noise level at full throttle would most definitely exceed the limit. The measurement procedure requires that more than one measurement of the sound level be taken. One of the reasons for this is to eliminate erroneous measurements form possibly faulty instruments or field conditions. With the use of a number of microphones, it may be possible to compare several recorded sound levels to eliminate faulty levels. A smaller microphone array aperture in this case will yield more similar sound levels.
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Automatic Vehicle Detection and Recognition

Automatic Vehicle Detection and Recognition

But, these systems entirely depend on recognizing just one feature of the Vehicle and that is its Licence plate. The only unique feature of a Vehicle is its Licence Plate, but it can be easily tampered with. In 2012, more than 2000 Licence Plates were stolen off the cars in Edmonton and in the first six months of 2013, over 1,100 Licence Plates were reported stolen across the city of Edmonton. Calgary Police says that there is an increase of 80% of licence plate theft increase in 2015 as compared to 2014[34]. And these stolen plates can be used to perform various acts of malign nature and the owner can be falsely implicated for an act which he/she has not performed. This problem led to a system which recognizes the vehicle with not only just Licence plates but also tie another feature of the car with that recognition can help to eliminate such threats. In the approach taken in this thesis along with the Licence plate recognition, the shape of the vehicle is also taken into account. Such systems can be installed in facilities which rely on the correct authentication of the person to enter the premises.
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IOT Based Vehicle Theft Detection

IOT Based Vehicle Theft Detection

Abstract: It is aforesaid that property crimes can reach ten million. Of these, the vehicle is flat-top within the steal list and is often control altogether components of the planet. Several new technological developments have developed and new techniques are upgraded to beat this drawback. The ways concerned in vehicle stealing recognition are noted to any or all, as well as shields, to interrupt the system and steal the vehicle. This paper displays a mechanism to reduce the vehicle stealing. This system provides protection by using RFID card and authorized key .RFID reader is attached to the car door and the entry is granted only if the card is authorized. Keypad is attached to the engine and it starts only when the authorized key is entered. It will make the continuous buzzer sound when vehicle has been stolen or moved without the owner's knowledge. System provides periodic updates for registered users through thingsspeak.com. This facility is provided by sending GPS location through GPS technology for stealing vehicle tracking.
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Vehicle License Plate Detection: A Survey

Vehicle License Plate Detection: A Survey

bloom in past few decades. This has led to urbanization on larger scale; as a result the number of privately owned vehicles has grown exponentially so have the license plates. This has given rise to problem such as identification of particular vehicle from a group of vehicles. This paper presents License plate recognition algorithms that consist of following processing steps: I) Extraction of LP region II) Segmentation of LP characters III) Detection of LP characters. The task to identify license plates is quite challenging as it depends on various external factors like Fonts, colors, size, non uniform outdoor lights, weather conditions etc. Thus, most approaches work only under specific conditions such as limited vehicle speed, good illuminations, and ideal weather conditions. Eventually techniques have been developed for License plate recognition using image processing in real time arrangement. Extraction of LP region is an important aspect of intelligent traffic system, as all countries in the world have adopted a uniform way to identify vehicle with help of License Plate system. The size of license plate varies from vehicle to vehicle or country to country. To identify the size of characters segmentation of the captured image is necessary in intelligent traffic system. Each country has its own method to provide License Number to Vehicles. Hence to identify them correctly every country will require a unique way to identify these license plates with the help of character recognition in intelligent traffic system. This will reduce the chaos of identification of vehicles.
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