Robust lane detection and tracking using improved Hough transform and Gaussian Mixture Model

2011 ◽  
Author(s):  
Yun Zhang ◽  
Junbin Gong ◽  
Jinwen Tian
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Hough Transform ◽  
Gaussian Mixture ◽  
Lane Detection ◽  
Detection And Tracking

Moving Object Detection and Tracking in outside Environments

2011 ◽  
Vol 130-134 ◽  
pp. 3862-3865
Author(s):  
Yi Ding Wang ◽  
Da Qian Li
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture ◽  
Middle Ground ◽  
Moving Objects Detection ◽  
Detection And Tracking ◽  
Objects Detection ◽  
Typical Method

Background subtraction is a typical method for moving objects detection. The Gaussian mixture model is one of widely used method to model the background. However, in challenge environments, quick lighting changes, noises and shake of background can influence the detection of moving objects significantly. To solve this problem, an improved Gaussian Mixture Model is proposed in this paper. In the proposed algorithm, Objects are divided into three categories, foreground, background and middle-ground. The proposed algorithm is a segmented process. Moving objects including foreground and middle-ground are extracted firstly; then foreground is segmented from middle-ground. In this way almost middle-ground are filtered, so we can obtain a clear foreground objects. Experimental results show that the proposed algorithm can detect moving objects much more precisely, and it is robust to lighting changes and shadows.

The Study on the Method of Vehicle Detection and Tracking Based on Gaussian Mixture Model and Kalman Filter

2014 ◽  
Vol 644-650 ◽  
pp. 1266-1269 ◽  
Author(s):  
Li Yuan Lin ◽  
Lin Chen
Keyword(s):  
Kalman Filter ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Vehicle Detection ◽  
Gaussian Mixture ◽  
Detection And Tracking ◽  
Frame Difference ◽  
Vehicle Detection And Tracking ◽  
Target Extraction ◽  
Detection Stage

In the vehicle detection stage,in order to solve the problem of Gaussian Mixture Model having poor adapting capacity on the sudden changes of the illumination,this paper designs a illumination judgement factor.When the factor is greater than a certain threshold,this paper uses three frame difference method for target extraction,otherwise uses the Gaussian Mixture Model.In the vehicle tracking stage,the Kalman Filter is introduced to improve tarcking accuracy and tracking efficiency,at the same time,a tracking list is designed for single and multi objectives tracking.The results show that the method can adapte the sudden changes of the illumination and can achive a better effect of vehicle detection and tracking.

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