4,234 research outputs found

    Survey of Various Methods used for Speed Calculation of a Vehicle

    Get PDF
    It is a survey paper of various method used for speed calculation of vehicles. The major purpose of vehicle speed detection is to provide a number of ways that law enforcement agencies can enforce traffic speed laws. The most famous methods include using RADAR (Radio Detection and Ranging) and LIDAR (Light Detection and Ranging) devices to detect the speed of a vehicle. RADAR use microwaves pules and LIDAR use coherent light beam for speed calculation. The SDCS (Speed Detection Camera System) and SMBI (Single Motion Blurred Image) method are also use on high traffic area to measure speed of vehicle using video stream and single image captured by stationary camera. DOI: 10.17762/ijritcc2321-8169.150314

    A Novel Approach for Image Deblurring

    Get PDF
    In the area of image processing blur removal is essential step in image quality enhancement .It also has real time applications, therefore it is necessary to have efficient method to remove blur. We have proposed a non linear blur model which simply models low light pixels. In this work we have applied Gaussian kernel instead of Laplacian kernel. The proposed method is developed in such a way that it automatically detects low light pixel from a given blurred image. It also suppress the ringing artifacts. The more accurate results are obtained on problematic and challenging blur images

    Automatic vehicle detection and tracking in aerial video

    Get PDF
    This thesis is concerned with the challenging tasks of automatic and real-time vehicle detection and tracking from aerial video. The aim of this thesis is to build an automatic system that can accurately localise any vehicles that appear in aerial video frames and track the target vehicles with trackers. Vehicle detection and tracking have many applications and this has been an active area of research during recent years; however, it is still a challenge to deal with certain realistic environments. This thesis develops vehicle detection and tracking algorithms which enhance the robustness of detection and tracking beyond the existing approaches. The basis of the vehicle detection system proposed in this thesis has different object categorisation approaches, with colour and texture features in both point and area template forms. The thesis also proposes a novel Self-Learning Tracking and Detection approach, which is an extension to the existing Tracking Learning Detection (TLD) algorithm. There are a number of challenges in vehicle detection and tracking. The most difficult challenge of detection is distinguishing and clustering the target vehicle from the background objects and noises. Under certain conditions, the images captured from Unmanned Aerial Vehicles (UAVs) are also blurred; for example, turbulence may make the vehicle shake during flight. This thesis tackles these challenges by applying integrated multiple feature descriptors for real-time processing. In this thesis, three vehicle detection approaches are proposed: the HSV-GLCM feature approach, the ISM-SIFT feature approach and the FAST-HoG approach. The general vehicle detection approaches used have highly flexible implicit shape representations. They are based on training samples in both positive and negative sets and use updated classifiers to distinguish the targets. It has been found that the detection results attained by using HSV-GLCM texture features can be affected by blurring problems; the proposed detection algorithms can further segment the edges of the vehicles from the background. Using the point descriptor feature can solve the blurring problem, however, the large amount of information contained in point descriptors can lead to processing times that are too long for real-time applications. So the FAST-HoG approach combining the point feature and the shape feature is proposed. This new approach is able to speed up the process that attains the real-time performance. Finally, a detection approach using HoG with the FAST feature is also proposed. The HoG approach is widely used in object recognition, as it has a strong ability to represent the shape vector of the object. However, the original HoG feature is sensitive to the orientation of the target; this method improves the algorithm by inserting the direction vectors of the targets. For the tracking process, a novel tracking approach was proposed, an extension of the TLD algorithm, in order to track multiple targets. The extended approach upgrades the original system, which can only track a single target, which must be selected before the detection and tracking process. The greatest challenge to vehicle tracking is long-term tracking. The target object can change its appearance during the process and illumination and scale changes can also occur. The original TLD feature assumed that tracking can make errors during the tracking process, and the accumulation of these errors could cause tracking failure, so the original TLD proposed using a learning approach in between the tracking and the detection by adding a pair of inspectors (positive and negative) to constantly estimate errors. This thesis extends the TLD approach with a new detection method in order to achieve multiple-target tracking. A Forward and Backward Tracking approach has been proposed to eliminate tracking errors and other problems such as occlusion. The main purpose of the proposed tracking system is to learn the features of the targets during tracking and re-train the detection classifier for further processes. This thesis puts particular emphasis on vehicle detection and tracking in different extreme scenarios such as crowed highway vehicle detection, blurred images and changes in the appearance of the targets. Compared with currently existing detection and tracking approaches, the proposed approaches demonstrate a robust increase in accuracy in each scenario

    Analysis and design of a capsule landing system and surface vehicle control system for Mars exploration

    Get PDF
    Problems related to the design and control of a mobile planetary vehicle to implement a systematic plan for the exploration of Mars are reported. Problem areas include: vehicle configuration, control, dynamics, systems and propulsion; systems analysis, terrain modeling and path selection; and chemical analysis of specimens. These tasks are summarized: vehicle model design, mathematical model of vehicle dynamics, experimental vehicle dynamics, obstacle negotiation, electrochemical controls, remote control, collapsibility and deployment, construction of a wheel tester, wheel analysis, payload design, system design optimization, effect of design assumptions, accessory optimal design, on-board computer subsystem, laser range measurement, discrete obstacle detection, obstacle detection systems, terrain modeling, path selection system simulation and evaluation, gas chromatograph/mass spectrometer system concepts, and chromatograph model evaluation and improvement

    Detection of Unfocused Raindrops on a Windscreen using Low Level Image Processing

    No full text
    International audienceIn a scene, rain produces a complex set of visual effects. Obviously, such effects may infer failures in outdoor vision-based systems which could have important side-effects in terms of security applications. For the sake of these applications, rain detection would be useful to adjust their reliability. In this paper, we introduce the problem (almost unprecedented) of unfocused raindrops. Then, we present a first approach to detect these unfocused raindrops on a transparent screen using a spatio-temporal approach to achieve detection in real-time. We successfully tested our algorithm for Intelligent Transport System (ITS) using an on-board camera and thus, detected the raindrops on the windscreen. Our algorithm differs from the others in that we do not need the focus to be set on the windscreen. Therefore, it means that our algorithm may run on the same camera sensor as the other vision-based algorithms

    An Efficient Direction Field-Based Method for the Detection of Fasteners on High-Speed Railways

    Get PDF
    Railway inspection is an important task in railway maintenance to ensure safety. The fastener is a major part of the railway which fastens the tracks to the ground. The current article presents an efficient method to detect fasteners on the basis of image processing and pattern recognition techniques, which can be used to detect the absence of fasteners on the corresponding track in high-speed(up to 400 km/h). The Direction Field is extracted as the feature descriptor for recognition. In addition, the appropriate weight coefficient matrix is presented for robust and rapid matching in a complex environment. Experimental results are presented to show that the proposed method is computation efficient and robust for the detection of fasteners in a complex environment. Through the practical device fixed on the track inspection train, enough fastener samples are obtained, and the feasibility of the method is verified at 400 km/h

    A practical multirobot localization system

    Get PDF
    We present a fast and precise vision-based software intended for multiple robot localization. The core component of the software is a novel and efficient algorithm for black and white pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost cameras, the core algorithm is able to process hundreds of images per second while tracking hundreds of objects with a millimeter precision. In addition, we present the method's mathematical model, which allows to estimate the expected localization precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions is verified in several experiments. Apart from the method description, we also make its source code public at \emph{http://purl.org/robotics/whycon}; so, it can be used as an enabling technology for various mobile robotic problems

    Intraframe Scene Capturing and Speed Measurement Based on Superimposed Image: New Sensor Concept for Vehicle Speed Measurement

    Get PDF
    A vision based vehicle speed measurement method is presented in this paper. The proposed intraframe method calculates speed estimates based on a single frame of a single camera. With a special double exposure, a superimposed image can be obtained, where motion blur appears significantly only in the bright regions of the otherwise sharp image. This motion blur contains information of the movement of bright objects during the exposure. Most papers in the field of motion blur are aiming at the removal of this image degradation effect. In this work, we utilize it for a novel speed measurement approach. An applicable sensor structure and exposure-control system are also shown, as well as the applied image processing methods and experimental results. © 2016 Mate Nemeth and Akos Zarandy
    corecore