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TYAs paper looks at some of the algorithms that can be used for effective detection and tracking of vehicles, in particular for statistical analysis. The main methods for tracking discussed and implemented are blob analysis, optical flow and foreground detection. A further analysis is also done testing two of the techniques using a number of video sequences that include different levels of difficulties.
Number of vehicles on road is very important traffic data and is essential for transportation safety and management. In this paper, an approach for vehicle detection is presented. In this approach, virtual line based sensors which are just straight detection lines are first set on road lanes. Then two features, namely gradient and range feature, are proposed for vehicle detection. This is carried...
This paper proposes a method to track vehicle in highway using CAMShift-based method. The Continuously Adaptive Mean Shift (CAMShift) is a well-known algorithm in object tracking. However, the ordinary CAMShift works fairly well only for tracking object that can identify by hue, when the difference between object color and background is large. This is not the case in vehicle tracking. The objective...
Vehicle detection in video is an important problem in Computer Vision because of the potential applications in security, vehicle traffic, driving assistance and so on. In this work, we used Mixture of Deformable Part Models (MDPM) for vehicle detection in video sequences obtained from static and dynamic cameras. The MDPM method was originally proposed by Felzenszwalb et al in the realm of object detection...
Among the diverse applications of computer and communication technologies, Intelligent Transport System aids in simplifying transport problems. Its aim is to gather data and provide timely feedback to traffic managers (traffic policemen) and road users. The various problems involved in processing real-time traffic data has been addressed in several areas of research that includes vehicle detection,...
A novel method for extracting traffic flow parameters for multi-lane roads is proposed. The presented method based on the technology of virtual loops. Firstly, complete of target vehicle information are extracted from traffic video sequences by using the background subtraction technique and shadow model in color space. Secondly, vehicles are detected through the virtual loops in gray space. Meanwhile,...
In computer vision-based Intelligent Transportation Systems (ITS), one of the key techniques is to exactly detect the vehicles. The common way for vehicle detection is the background difference method. In this paper, we introduce several background extraction and updating methods and develop a new background extraction and update method based on histogram in YCbCr color space. By using YCbCr color...
Detection and classification of vehicles are the most challenging tasks of a video-based intelligent transportation system. Traditional detection and classification methods are based on subtraction of estimated still backgrounds from a video to find out the moving objects. In general, these methods are computationally highly expensive, and in many cases show poor detection and classification performance,...
A vision-based vehicle detection method is presented in this paper. The proposed method is composed of two steps, i.e., hypothesis generation and hypothesis verification. An adaptive background modeling and updating method is proposed to detect foreground regions in video sequences. With the prior knowledge of the vehicle appearance, the possible vehicle locations are extracted from the foreground...
In this paper, a multistage algorithm, which is applicable to vision-based vehicle detection system, for detecting and removing cast shadows is proposed. In the first stage, a novel noise level adapted method is presented to segment foreground which consists the moving objects and moving shadows. In the second stage, the foreground is partitioned into sub-regions using multilevel thresholding. Then,...
This paper proposes a new road traffic monitoring method based on image processing and particle filtering. The proposed method detects and classifies automatically moving vehicles in previously defined classes. The detected vehicles are tracked using a new particle filtering algorithm to determine their positions on the road at each time, and then the vehicle positions are used to estimate its trajectory...
This paper presents a novel approach to vehicle detection in highway surveillance videos. This method incorporates well-studied computer vision and machine learning techniques to form an unsupervised system, where vehicles are automatically ldquolearnedrdquo from video sequences. First an enhanced adaptive background mixture model is used to identify positive and negative examples. Then a classifier...
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