Moving Objects Detection and Tracking with Camera Motion Compensation

2020 ◽  
pp. 1193-1210
Author(s):  
Bousta Mohamed Akli ◽  
Nemra Abdelkrim ◽  
Hamami Fatima ◽  
Demim Fethi
Keyword(s):  
Motion Compensation ◽  
Moving Objects ◽  
Camera Motion ◽  
Moving Objects Detection ◽  
Detection And Tracking ◽  
Objects Detection

Moving Objects Detection and Tracking Framework for UAV-based Surveillance

2010 ◽  
Author(s):  
Aryo Wiman Nur Ibrahim ◽  
Pang Wee Ching ◽  
G.L. Gerald Seet ◽  
W.S. Michael Lau ◽  
Witold Czajewski
Keyword(s):  
Moving Objects ◽  
Moving Objects Detection ◽  
Detection And Tracking ◽  
Objects Detection

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.

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