Real-time moving objects detection and tracking from airborne infrared camera

2017 ◽  
Author(s):  
Marco Diani ◽  
Giovanni Corsini ◽  
Andrea Zingoni
Keyword(s):  
Real Time ◽  
Moving Objects ◽  
Infrared Camera ◽  
Moving Objects Detection ◽  
Detection And Tracking ◽  
Objects Detection

ELITE: A goal oriented vision system for moving objects detection

Robotica ◽  
1991 ◽  
Vol 9 (3) ◽  
pp. 275-282 ◽  
Author(s):  
N. A. Borghese ◽  
M. Di Rienzo ◽  
G. Ferrigno ◽  
A. Pedotti
Keyword(s):  
Real Time ◽  
Convolution Operator ◽  
Moving Objects ◽  
Vision System ◽  
Moving Objects Detection ◽  
Real Time Image ◽  
Specific Shape ◽  
Objects Detection ◽  
Time Image ◽  
Movement Monitoring

SUMMARYA specially designed system for movement monitoring is here presented. The system has a two level architecture. At the first level, a hardware processor analyses in real-time the images provided by a set of standard TV cameras and, using a technique based on the convolution operator, recognizes in each frame objects that have a specific shape. The coordinates of these objects are fed to a computer, the second level of the system, that analyses the movement of these objects with the aid of a set of rules representing the knowledge of the context. The system was extensively tested on the field and the main results are reported.The whole system can work as a controlling device in robotics or as a general real-time image processor as well as an automatic movement analyser in biomechanics, orthopedic and neurological medicine.

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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