Top PDF Vehicle Tracking Using Image Processing

Vehicle Tracking Using Image Processing

Vehicle Tracking Using Image Processing

Automatic identification of vehicle data has been commonly used in the vehicle information system and intelligent traffic system. It has acquired more attention of researchers from the last decade with the advancement of digital imaging technology and computational capacity. Automatic vehicle detection systems are keys to road traffic control nowadays; some applications of these systems are traffic response system, traffic signal controller, lane departure warning system, automatic vehicle accident detection and automatic traffic density estimation. An Automatic vehicle checking framework makes utilization of video information gained from stationary movement cameras, performing causal numerical operations over an arrangement of outlines got from the video to gauge the quantity of vehicles display in a scene. It is only the capacity of consequently remove and perceive the activity information e.g. add up to number of vehicles, vehicle number and name from a video. Checking vehicles gives us the data expected to get an essential comprehension over the stream of activity in any district under observation. Along these lines, the primary information we have attempted to accumulate is tallying of vehicles from accessible movement recordings from different libraries. In every video outline, Gaussian blend show separates protests in movement from the foundation by following identified protests inside a particular locale of the edge, and afterward forgetting about is conveyed.
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12. Smart Card and Image Processing Based Vehicle Tracking System

12. Smart Card and Image Processing Based Vehicle Tracking System

Abstract: Now a day’s more number of vehicles traveled on roads, we cannot find the vehicle particular place. In addition, if any candidate theft vehicle we cannot find the vehicle place and thief so to overcome these problems by using Image processing and RFID vehicle tracking system. It consists of a RFID tag, which consists of owner name, place, communication details, mobile number and address. The RFID Tag is present on the roof of vehicle. Moreover, RFID reader, which is, reads the RFID tag information and save the details as vehicle details about RFID reader placed place after and send this information to central vehicle system office. In addition, a image scanner is present at the RFID reader it is used to scan the driver image and send the details like license number, name and address of driver to central system.
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Missile Burst Prevention and Tracking System Using Image Processing In MATLAB

Missile Burst Prevention and Tracking System Using Image Processing In MATLAB

ABSTRACT: The system is designed to track the missile and avoid the incidents where a single missile attack could cause the loss of thrust for an entire scheme. Here we are trying to develop a system to track the missile which ensuring the seeker and divert maneuver capabilities needed to remove system errors. The system will consist of two subsystems, a detection system and a deflection system. The detection system uses a camera and is connected to MATLAB and sends instruction for tracking in to microcontroller. The MATLAB reads the input image and is programmed to identify the presence of missile by analyzing the images using image processing. Once a missile is detected it activates the deflection system. The deflection system helps to detect the missile from following its current path thereby reducing the risk of missile tracking which has been implemented in MATLAB R2013a simulator.
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Reckoning the Vehicle using MATLAB

Reckoning the Vehicle using MATLAB

essential part of image processing. Knowing the variety of vehicles in the image can be useful for further evaluation in a huge variety of applications. In this venture we propose a simple technique for determining the wide variety of vehicle in an image. Existing strategies contain counting based totally on location of car, color of the vehicle, making use of part detection strategies etc. This mission work also goals at figuring out the right value of density by clearing the vehicle touching the borders of the image. In this challenge the usage of MATLAB with image processing toolbox the count and density values are calculated and estimating the proper count. Automatic detecting and monitoring vehicles in video surveillance records is a totally tough thing in computer vision with essential practical programs, along with traffic evaluation and security. Manually reviewing the large amount of facts they generate is frequently impractical. Thus, algorithms for reading video which require little or no human enter is a scientific answer. Video surveillance structures are focused on monitoring, moving vehicle classification and tracking. A car tracking and classification system is described as one that could categorize transferring vehicles and similarly classifies that into various categories. Traffic control and statistics structures rely mainly on sensors for estimating the visitors parameters.
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Vehicle Tracking System Using GPS Tracking Technology

Vehicle Tracking System Using GPS Tracking Technology

GPS location module GS-87 is the third generation of GPS receiver chip designed by the United States SiRF star III company, which consists of a radio frequency integrated circuit, a digital signal processing circuit and standard embedded GPS software composition .Radio frequency integrated circuit is used to detect and process GPS RF signal. Digital signal processing circuit is used to process the IF signal. The standard embedded GPS software is used search and follow up GPS satellite signals, Users to coordinate and speed is available according to the information.[5] It is a high performance, low-power intelligent satellite receiver module or called satellite engine, is a complete GPS receiver.
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Automatics Vehicle License Plate Recognition
using MATLAB

Automatics Vehicle License Plate Recognition using MATLAB

Abstract - The objective of this paper is to be able to detect the area of a car license in a photograph. Using computer vision and digital image processing techniques. The different processes to which a photograph of a car is subjected will be explained in order to detect its registration. It will be indicated under what conditions the presented algorithm is effective and correctly detects the registration and under what conditions it can produce false positives. Indicate that the algorithm will only detect the area where the license plate is located, adjusting as much as possible the size of this zone to the size of the license plate.
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Survey on Video Object Detection & Tracking
                 

Survey on Video Object Detection & Tracking  

Cucchiara et al. [10] proposed an approach for detecting Vehicles in urban traffic scenes by means of rule-based reasoning on visual data. The strength of the approach is its formal separation between the low- level image processing modules (used for extracting visual data under various illumination conditions) and the high- level module, which provides a general purpose knowledge- based framework for tracking vehicles in the scene. The image-processing modules extract visual data from the scene by spatial-temporal analysis during daytime and by morphological analysis of headlights at night. The high- level module is designed as a forward chaining production rule system, working on symbolic data, i.e., vehicles and their attributes (area, pattern, direction, and others) and exploiting a set of heuristic rules tuned to urban traffic conditions. The synergy between the artificial intelligence techniques of the high- level and the low- level image analysis techniques provides the system with flexibility and robustness.
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Directions Aids for the Visually Challenged Using
Image Processing Using Image Recognition

Directions Aids for the Visually Challenged Using Image Processing Using Image Recognition

The first sonar-based mobility aid was the handheld sonic torch, using a special ultrasonic sensor developed by Leslie Kay in the early 1960s. The cell-phone-sized device can be affixed to the handle of a long cane, increasing its effective range to detection of a 40 mm diameter object out to 5 m. With the help of this technology, a user is able to hear echoes from multiple sources, facilitating simultaneous tracking of more than one object in the

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Applied Techniques in Tracking Moving Targets in Marine Using Image Processing

Applied Techniques in Tracking Moving Targets in Marine Using Image Processing

System not based on model separate moving area from image through movement detection methods, therefore if these spots have suitable specifications, are assumed as target and tracked in [r]

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Vehicle Recognition at Night Based on Tail LightDetection Using Image Processing

Vehicle Recognition at Night Based on Tail LightDetection Using Image Processing

This entire process essentially amounts to a symmetry check.If a bounding Box above a certain size is detected then the driver is alerted that a vehicle is close. This section presents some preliminary results drawn from video of a real road environment.In an 12 second sample video of 200 frames,the target vehicle was approximately 10m ahead. The vehicle in 164 of 172 frames, resulting in a detection rate of 98 percent. Bounding boxes resulting from white regions appeared incorrectly,not identifying the target vehicle,4 times in the 200 frames, resulting in a false positive rate of 1.8 percent. These results refer to only a subset of the total video data. The algorithm has demonstrated that it works well in both well lit urban areas and dark rural areas. It also works effectively in wet conditions.
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Lane detection system for autonomous vehicle using image processing techniques

Lane detection system for autonomous vehicle using image processing techniques

Analyze and understand the condition of the lane marking white lines on dark pavement of the road by extracting all necessary infOimation from the image of the road.... This system shoul[r]

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Real time 3D Face Recognition using Line Projection and Mesh Sampling

Real time 3D Face Recognition using Line Projection and Mesh Sampling

It is often stated that 3D facial recognition has potential advantages over 2D methods [2, 6] as a number of limitations can be overcome including lighting variations and viewpoint dependency [9, 10]. Moreover, 3D data can provide high accuracy on describing surface features such as cheeks and nose through curvature descriptors [11]. This paper describes a real-time, fully automatic 3D face recognition system: from 2D eye tracking and image capture to 3D reconstruction, to 3D post-processing and recognition. It is noted that recognition is based on geometry alone: once an image is reconstructed in 3D, no texture information is used in the recognition process.
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An approach for moving object tracking in image processing methods

An approach for moving object tracking in image processing methods

A BSTRACT : Image processing is processing of images using mathematical operations by using any form of signal processing for which the input is an image, a series of images, or a video ,such as photograph or video frame, the output of image processing may be either an image or a set of characteristics or parameters related to the image. This paper focus on moving object tracking method, this method is used for describe a moving object in a tracking algorithm. Color, motion information, edge and texture are common features to represent moving objects. Moving object detection method, this method is used to extract moving object in video image sequences. Detecting the moving and sounding objects via utilization of canonical correlation analysis (CCA) method, it is utilized to identify the moving objects which are most correlated to the audio signal. The search & detection of moving objects method is used for addresses the detection of moving objects that could interfere with driver behavior, either through distraction or by posing an actual danger. Digital image correlation (DIC) method using fisheye lens, this method is used to inspect the interior wall displacement and strain of hollow cylinder, the fisheye lens provides a long depth of field and wide angle of view that make it suitable for this work. The proposed method can give better performance in result.
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Parking Lot Occupancy Tracking Through Image Processing

Parking Lot Occupancy Tracking Through Image Processing

This method had better results than using color heuristics on lighter colored vehicles, but still produced false negatives often. It was difficult to see edges in low lighting or dark colored vehicles. Edge detection also gave several false positives as a result. Figure is a typical visualized output of using the edge detection method. This was run on a limited number of parking lot images for the purposed of gaining knowledge in its effectiveness. Accuracy averaged at 62% for the 10 images tested. This average depended on the concentration of light vehicles to dark vehicles present in the lot.
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An Image Processing Based Method For Vehicle Speed Estimation

An Image Processing Based Method For Vehicle Speed Estimation

may encounter during speed estimation which makes this method, a less accurate. When the direction of the radar gun is not on the direct path of the incoming vehicle, cosine error may occur. Shading (radar wave reflection from two different vehicles with distinctive heights), and radio interference errors are there. The cost of equipment is also one of the important reasons. Furthermore, RADAR sensors can track only one vehicle at a time. Second types are methods uses image processing and camera to measure the speed; they do not use any special hardware. Now we will discuss the some important research works of both types, done till date in the field of speed estimation
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VIDEO PRE-PROCESSING OF IMAGE INFORMATION FOR VEHICLE IDENTIFICATION

VIDEO PRE-PROCESSING OF IMAGE INFORMATION FOR VEHICLE IDENTIFICATION

Low pass filtering involves the elimination of the high frequency components in the image. Linear filtering can improve images in many ways: sharpening the edges of objects, reducing random noise, correcting for unequal illumination, and not convolution to correct for blur and motion, etc. These procedures are carried out by convolving the original image with an appropriate filter kernel, producing the filtered image. A serious problem with image convolution is the enormous number of calculations that need to be performed, often resulting in unacceptably long execution times. Low pass filter lack the capabilities for performing image analysis.
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Vehicle Counting and Automated Toll Collection
System using Image Processing

Vehicle Counting and Automated Toll Collection System using Image Processing

The RFID reader will be placed strategically at the toll plaza. When the vehicle crosses the toll plaza, the toll amount based on vehicle type will be deduced from user’s account balance and new account balance will be updated. If the user has insufficient balance, they uses the alarm which will alert the authority that the vehicle have insufficient balance and that particular vehicle can be trapped. The advantage of this system is RFID tag can not be cloned so can not be cheated and it is less costly. The disadvantage of this system is RFID tag are vulnerable to electro static discharge damage.
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Indian Vehicle Number Plate Detection Using Image Processing

Indian Vehicle Number Plate Detection Using Image Processing

Vehicle Number Plate Identification (VNPI) is a part of digital image processing which is generally used in vehicle transportation system to categorize the vehicle. Number plate recognition systems are having varieties of application such as traffic maintenances, tracing stolen cars, automatic electronic Toll collection system etc. But the main aim is to control the traffic management system. In India the traffic management system is developing day by day. In India, the number plate containing white background with black foreground color is used for private cars and for the commercial vehicles yellow is used as background and black as foreground color. The number plate starts with two digit letter “state code” followed by two digit numeral, followed by single letter after those four following digits as the below figure1.1.
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Index Terms - Finding modes, Image segmentation, Image processing, Space analysis & Object tracking, Visual tracking.

Index Terms - Finding modes, Image segmentation, Image processing, Space analysis & Object tracking, Visual tracking.

In image segmentation process clustering is needed to find the neighboring data. Cluster analysis or clustering is the assignment of the objects into clusters which makes the objects from a particular cluster more similar to themselves by differing themselves from the objects of other clusters. This similarity between the objects of the same cluster often assessed according to a distant measure. For statistical data analysis, clustering is an often-employed technique. Moreover, this technique is used in many fields including machine learning, data mining, image analysis, pattern recognition, and bioinformatics. It is the process to divide data elements into diverse clusters or classes so that items of a same class areas become as similar as possible; on the other hand, items of the different classes become dissimilar according their disparity. Different measures of similarity could be used to place items into classes depending on the nature of the data and of the purpose for which clustering is used. Similarity measure determines how the clusters are to be formed. It is to say that there are different types of clustering techniques which are described below [5], [11], [12].
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Automatic cattle location tracking using image processing

Automatic cattle location tracking using image processing

Despite the desirable advantages of RTCT such as low complexity and its high success rate compared to other algorithms in the literature [5-11], this implementation revealed several issues, including some like those previ- ously encountered [12], that can prevent the cow from being accurately tracked. Firstly, we reiterate that RTCT has been developed for tracking an object in a single cam- era. Therefore, the localisation results from different cam- eras are obtained independently and the combination of them needs to be considered carefully in order to obtain more accurate results. Beyond this, there are several com- mon situations when the tracked object will be easily lost, for example: when it moves out of view of the camera; when it is completely hidden or occluded by another ani- mal; when its appearance changes quickly. Unfortunately, these situations happen very often in cow tracking; and this leads to a high loss rate when using the original RTCT algorithm.
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