A novel shadow elimination algorithm for moving object segmentation

2006 ◽  
Author(s):  
Bo Li ◽  
Baozong Yuan ◽  
Yunda Sun ◽  
Chengkai Wan
Keyword(s):  
Object Segmentation ◽  
Moving Object ◽  
Moving Object Segmentation ◽  
Shadow Elimination ◽  
Elimination Algorithm

scholarly journals AN ADAPTIVE BACKGROUND UPDATION AND GRADIENT BASED SHADOW REMOVAL METHOD

2013 ◽  
pp. 288-293
Author(s):  
SUMIT KUMAR SINGH ◽  
MAGAN SINGH
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Object Segmentation ◽  
Moving Object ◽  
Shadow Removal ◽  
Subtraction Method ◽  
Moving Object Segmentation ◽  
Shadow Elimination ◽  
Illumination Changes ◽  
Gradient Based

Moving object segmentation has its own niche as an important topic in computer vision. It has avidly being pursued by researchers. Background subtraction method is generally used for segmenting moving objects. This method may also classify shadows as part of detected moving objects. Therefore, shadow detection and removal is an important step employed after moving object segmentation. However, these methods are adversely affected by changing environmental conditions. They are vulnerable to sudden illumination changes, and shadowing effects. Therefore, in this work we propose a faster, efficient and adaptive background subtraction method, which periodically updates the background frame and gives better results, and a shadow elimination method which removes shadows from the segmented objects with good discriminative power. Keywords- Moving object segmentation,

Moving object segmentation algorithm for river surveillance video

2011 ◽  
Vol 31 (1) ◽  
pp. 263-265
Author(s):  
Li-hua YE ◽  
Hai-ming YIN ◽  
Yuan-wang WEI
Keyword(s):  
Object Segmentation ◽  
Moving Object ◽  
Segmentation Algorithm ◽  
Surveillance Video ◽  
Moving Object Segmentation

scholarly journals U2-ONet: A Two-Level Nested Octave U-Structure Network with a Multi-Scale Attention Mechanism for Moving Object Segmentation

2020 ◽  
Vol 13 (1) ◽  
pp. 60
Author(s):  
Chenjie Wang ◽  
Chengyuan Li ◽  
Jun Liu ◽  
Bin Luo ◽  
Xin Su ◽  
...  
Keyword(s):  
Moving Objects ◽  
Object Segmentation ◽  
Contextual Information ◽  
Attention Mechanism ◽  
Moving Object ◽  
Feature Maps ◽  
Moving Object Segmentation ◽  
Practical Applications ◽  
Multi Scale ◽  
Spatial Redundancy

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.

Moving object segmentation for jittery videos, by clustering of stabilized latent trajectories

2017 ◽  
Vol 64 ◽  
pp. 10-22 ◽  
Author(s):  
Geethu Miriam Jacob ◽  
Sukhendu Das
Keyword(s):  
Object Segmentation ◽  
Moving Object ◽  
Moving Object Segmentation ◽  
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