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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...
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...
Aiming at improving readability of the license plates in some real traffic surveillance video, a system of location and super-resolution of license plates is presented. After modeling the background by Gaussian mixture model and getting the vehicle region from the video, the license plates are located by connected component analysis and tracked by template matching. Then, the super-resolution reconstruction...
This article introduces a new particle filtering approach for object tracking in video sequences. The projective particle filter uses a linear fractional transformation, which projects the trajectory of an object from the real world onto the camera plane, thus providing a better estimate of the object position. In the proposed particle filter, samples are drawn from an importance density integrating...
The paper introduces background extractions with self-adaptive update algorithm and puts forward an improved algorithm based on histogram statistic combining with multi-frame average. It avoids the image trail-blur phenomena using pure multi-frame method on traffic jam, and has relative low computation complexity comparing with the hybrid Gauss model. It can run on the TI DM642 DSP hardware platform,...
A shadow detection algorithm base on spatial features was proposed, in the case of focusing on traffic vehicle detection system. First of all, multi-foreground rectangles were extracted by using Gaussian mixture model (GMM) and edge detection operator of mathematical morphology. Then, histogram of horizontal location - foreground point number of vertical direction was computed, combined with optimum...
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