Change detection for moving object segmentation with robust background construction under Wronskian framework

2013 ◽  
Vol 24 (4) ◽  
pp. 795-809 ◽  
Author(s):  
Badri Narayan Subudhi ◽  
Susmita Ghosh ◽  
Ashish Ghosh
2004 ◽  
Vol 40 (13) ◽  
pp. 798 ◽  
Author(s):  
J.-C. Huang ◽  
T.-S. Su ◽  
L.-J. Wang ◽  
W.-S. Hsieh

2020 ◽  
Vol 13 (1) ◽  
pp. 60
Author(s):  
Chenjie Wang ◽  
Chengyuan Li ◽  
Jun Liu ◽  
Bin Luo ◽  
Xin Su ◽  
...  

Most scenes in practical applications are dynamic scenes containing moving objects, so accurately segmenting moving objects is crucial for many computer vision applications. In order to efficiently segment all the moving objects in the scene, regardless of whether the object has a predefined semantic label, we propose a two-level nested octave U-structure network with a multi-scale attention mechanism, called U2-ONet. U2-ONet takes two RGB frames, the optical flow between these frames, and the instance segmentation of the frames as inputs. Each stage of U2-ONet is filled with the newly designed octave residual U-block (ORSU block) to enhance the ability to obtain more contextual information at different scales while reducing the spatial redundancy of the feature maps. In order to efficiently train the multi-scale deep network, we introduce a hierarchical training supervision strategy that calculates the loss at each level while adding knowledge-matching loss to keep the optimization consistent. The experimental results show that the proposed U2-ONet method can achieve a state-of-the-art performance in several general moving object segmentation datasets.


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