16-03-2012, 04:03 PM
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ABSTRACT:
This seminar presents algorithms for vision-based detection and classification of vehicles in monocular image sequences of traffic scenes recorded by a stationary camera. Processing is done at three levels: raw images, region level and vehicle level. Vehicles are modeled as rectangular patches with certain dynamic behavior. The proposed method is based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence. Experimental results from highway scenes are provided which demonstrate the effectiveness of the method. An interactive camera calibration tool is used for recovering the camera parameters using features in the image selected by the user.
1. INTRODUCTION:
A system that automatically capturing image of a moving vehicle and recording data parameters, such as date, time, speed operator, location, etc. on the image. A capture window that comprises a predetermined range of distances of the system from the moving vehicle can be set by the operator so that the image of the moving vehicle is automatically captured when it enters the capture window. The capture window distance can be entered manually through a keyboard or automatically using the laser speed gun. Automatic focusing is provided using distance information from the laser speed gun. Traffic management and information systems rely on a suite of sensors for estimating traffic parameters. Magnetic loop detectors are often used to count vehicles passing over them. Vision-based video monitoring systems offer a number of advantages. In addition to vehicle counts, a much larger set of traffic parameters such as vehicle classifications, lane changes, etc. can be measured. Besides, cameras are much less disruptive to install than loop detectors. Vehicle classification is important in the computation of the percentages of vehicle classes that use state-aid streets and highways. The current situation is described by outdated data and often, human operators manually count vehicles at a specific street. The use of an automated system can lead to accurate design of pavements (e.g., the decision about thickness) with obvious results in cost and quality.
TIME DIFFERENCING APPROACH
? It consists of subtracting successive frames (or frames a fixed interval apart).
? This method is insensitive to lighting conditions and has the advantage of not requiring initialization with a background image.
? However, this method produces many small regions that can be difficult to separate from noise.
SELF-ADAPTIVE BACKGROUND SUBTRACTION
A self-adaptive background subtraction method is used for segmentation. This method automatically extracts the background from a video sequence and so manual initialization is not required. This segmentation technique consists of three tasks: