A Modified Gaussian Mixture Background Model for Moving Object Detection

2017 ◽  
Vol 14 (8) ◽  
pp. 3672-3678 ◽  
Author(s):  
Mansoor Ahmed Khuhro ◽  
Dongjun Huang ◽  
Shaonian Huang ◽  
Papias Niyigena ◽  
Ammar Oad
Keyword(s):  
Object Detection ◽  
Gaussian Mixture ◽  
Moving Object Detection ◽  
Moving Object ◽  
Background Model

scholarly journals Moving Object Detection for Dynamic Background Scenes Based on Spatiotemporal Model

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yizhong Yang ◽  
Qiang Zhang ◽  
Pengfei Wang ◽  
Xionglou Hu ◽  
Nengju Wu
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Moving Object Detection ◽  
Moving Object ◽  
Background Model ◽  
Dynamic Background ◽  
Spatiotemporal Model ◽  
Temporal Features ◽  
Common Technique ◽  
Public Datasets

Moving object detection in video streams is the first step of many computer vision applications. Background modeling and subtraction for moving detection is the most common technique for detecting, while how to detect moving objects correctly is still a challenge. Some methods initialize the background model at each pixel in the first N frames. However, it cannot perform well in dynamic background scenes since the background model only contains temporal features. Herein, a novel pixelwise and nonparametric moving object detection method is proposed, which contains both spatial and temporal features. The proposed method can accurately detect the dynamic background. Additionally, several new mechanisms are also proposed to maintain and update the background model. The experimental results based on image sequences in public datasets show that the proposed method provides the robustness and effectiveness in dynamic background scenes compared with the existing methods.

Moving object detection zone using a block‐based background model

2017 ◽  
Vol 12 (1) ◽  
pp. 86-94 ◽  
Author(s):  
Omar Elharrouss ◽  
Abdelghafour Abbad ◽  
Driss Moujahid ◽  
Hamid Tairi
Keyword(s):  
Object Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Background Model ◽  
Detection Zone ◽  
Block Based

scholarly journals Moving Object Detection and Shadow Removing under Changing Illumination Condition

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Jinhai Xiang ◽  
Heng Fan ◽  
Honghong Liao ◽  
Jun Xu ◽  
Weiping Sun ◽  
...  
Keyword(s):  
Object Detection ◽  
Intensity Ratio ◽  
Moving Objects ◽  
Spatial Relation ◽  
Gaussian Mixture ◽  
Moving Object Detection ◽  
Moving Object ◽  
Illumination Change ◽  
Enhancement Method ◽  
Local Intensity

Moving object detection is a fundamental step in video surveillance system. To eliminate the influence of illumination change and shadow associated with the moving objects, we proposed a local intensity ratio model (LIRM) which is robust to illumination change. Based on the analysis of the illumination and shadow model, we discussed the distribution of local intensity ratio. And the moving objects are segmented without shadow using normalized local intensity ratio via Gaussian mixture model (GMM). Then erosion is used to get the moving objects contours and erase the scatter shadow patches and noises. After that, we get the enhanced moving objects contours by a new contour enhancement method, in which foreground ratio and spatial relation are considered. At last, a new method is used to fill foreground with holes. Experimental results demonstrate that the proposed approach can get moving objects without cast shadow and shows excellent performance under various illumination change conditions.

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