29-04-2011, 08:23 AM
i'm not getting information about the "Solving matrix equation for camera parameters"
29-04-2011, 08:23 AM
i'm not getting information about the "Solving matrix equation for camera parameters"
15-12-2012, 11:30 AM
REAL TIME IMAGE PROCESSING APPLIED TO TRAFFIC –QUEUE DETECTION ALGORITHM REAL TIME IMAGE PROCESSING.docx (Size: 429.21 KB / Downloads: 40) ABSTRACT: This paper primarily aims at the new technique of video image processing used to solve problems associated with the real-time road traffic control systems. There is a growing demand for road traffic data of all kinds. Increasing congestion problems and problems associated with existing detectors spawned an interest in such new vehicle detection technologies. But the systems have difficulties with congestion, shadows and lighting transitions. Problem concerning any practical image processing application to road traffic is the fact that real world images are to be processed in real time. Various algorithms, mainly based on back ground techniques, have been developed for this purposes since back ground based algorithms are very sensitive to ambient lighting conditions, they have not yielded the expected results. So a real-time image tracking approach using edged detection techniques was developed for detecting vehicles under these trouble-posing conditions. INTRODUCTION: . Digital processing is done with a digital computer or some special purpose digital hardware . The word Digital implies that the information in the computer is represented and sent by variables that taken limited number of discrete values. • increasing demand for road traffic data of all sorts. • Variation of parameters in real-world traffic • Aimed to measure queue parameters accurately • Algorithm has two operations: vehicle detection and motion detection • Operations applied to profiles consisting sub-profiles to detect queue • Motion detection is based on applying a differencing technique on the profiles of the images along the road • The vehicle detection is based on applying edge detection on these profiles Digital Signal Methods of vehicle detection: • Background frame differencing: -grey-value intensity reference image • Inter-frame differencing: -incoming frame itself becomes the background for the following frame • Segmentation and classification: -Sub division of an image into its constituent parts depending on the context Queue Detection Algorithm • Approach described here is a spatial-domain technique to detect queue - implemented in real-time using low-cost system • For this purpose two different algorithms have been used, Motion detection operation, Vehicle detection operation • Motion detection is first – as in this case vehicle detection mostly gives positive result, while in reality, there may not be any queue at all. Applying this scheme further reduces computation time. Left-limit selection program: - • This program selects a grey value from the histogram of the window, where there are approx. zero edge points above this grey value. • When the window contains an object, the left-limit of the histogram shifts towards the maximum grey value, otherwise it shifts towards the origin. • This process is repeated for a large no. of frames(100),and the minimum of the left-limit of these frames is selected as the left-limit for the next frame Conclusions • Algorithm measuring basic queue parameters such as period of occurrence between queues, the length and slope of occurrence has been discussed. • The algorithm uses a recent technique by applying simple but effective operations. • In order to reduce computation time motion detection operation is applied on all sub-profiles while the vehicle detection operation is only used when it is necessary. • The vehicle detection operation is a less sensitive edge-based technique. The threshold selection is done dynamically to reduce the effects of variations of lighting. • The measurement algorithm has been applied to traffic scenes with different lighting conditions. |
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