Infrared small target tracking by discriminative classification based on Gaussian mixture model in compressive sensing domain

2017 ◽  
Author(s):  
Chuanyun Wang ◽  
Fei Song ◽  
Shiyin Qin
Keyword(s):  
Compressive Sensing ◽  
Target Tracking ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Small Target ◽  
Infrared Small Target

scholarly journals Continuously Adaptive Mean-shift Tracking Algorithm Based on Improved Gaussian Model

2020 ◽  
Vol 13 (5) ◽  
pp. 50-57
Author(s):  
Jinping Sun ◽  
◽  
Enjie Ding ◽  
Dan Li ◽  
Kailiang Zhang ◽  
...  
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Mean Shift ◽  
Gaussian Mixture ◽  
Tracking Algorithm ◽  
Target Color ◽  
Color Model ◽  
Background Color ◽  
Complex Scenes

In complex scenes with light changes, deformations, and occlusions, target tracking easily contains a large amount of background color information when building a target color model. Thus, the tracking effect is reduced. To improve the accuracy of the traditional continuously adaptive mean-shift algorithm (CAMShift) in complex scenarios, a target tracking algorithm based on an improved Gaussian mixture model was proposed. Using the Gaussian mixture model, the tracking image was divided into the foreground and background superposition. The histograms of the hue component were respectively established in the foreground and background of the target area. By suppressing the same hue as the background color in the tracking image, the target color model was established. The target position was iteratively obtained by implementing the CAMShift algorithm using the enhanced target color model. The Bhattacharyya distance between the candidate target and the target template was used as basis for updating the target model. Simulation analysis under benchmark data sets and actual monitoring scenarios verified the accuracy of the proposed algorithm. Results show that the distance precision and overlap success rate of the proposed algorithm are 0.88 and 0.625, respectively. The proposed algorithm effectively solves long-term target tracking problems with complex scenes, such as occlusion, background clutters, and illumination variation. This study eliminates the problem of target recognition caused by environmental changes and provides references for real-time monitoring of abnormal traffic conditions.

Gaussian mixture model for video compressive sensing

2013 ◽  
Author(s):  
Jianbo Yang ◽  
Xin Yuan ◽  
Xuejun Liao ◽  
Patrick Llull ◽  
Guillermo Sapiro ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture

scholarly journals Statistical compressive sensing based on convolutional Gaussian mixture model

2019 ◽  
Vol 68 (18) ◽  
pp. 180701
Author(s):  
Ren Wang ◽  
Jing-Bo Guo ◽  
Jun-Peng Hui ◽  
Ze Wang ◽  
Hong-Jun Liu ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture

A new compressive sensing video coding framework based on Gaussian mixture model

2017 ◽  
Vol 55 ◽  
pp. 66-79 ◽  
Author(s):  
Xiangwei Li ◽  
Xuguang Lan ◽  
Meng Yang ◽  
Jianru Xue ◽  
Nanning Zheng
Keyword(s):  
Video Coding ◽  
Compressive Sensing ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture

scholarly journals Compressive Sensing by Learning a Gaussian Mixture Model From Measurements

2015 ◽  
Vol 24 (1) ◽  
pp. 106-119 ◽  
Author(s):  
Jianbo Yang ◽  
Xuejun Liao ◽  
Xin Yuan ◽  
Patrick Llull ◽  
David J. Brady ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture
Sign in / Sign up

Export Citation Format

Share Document