Particle Swarm Clustering-Based Moving Object Segmentation in the H.264 Compressed Domain

2012 ◽  
Vol 433-440 ◽  
pp. 4841-4844
Author(s):  
Pei Wang ◽  
Jing Wang
Keyword(s):  
Clustering Algorithm ◽  
Moving Objects ◽  
Object Segmentation ◽  
Median Filter ◽  
Particle Swarm ◽  
Moving Object ◽  
Compressed Domain ◽  
Motion Vectors ◽  
Moving Object Segmentation ◽  
Vector Median

An approach of the moving object segmentation is proposed in this paper. Firstly the motion fields are extracted from the compressed stream, where the noise and the unreal motion blocks are removed by vector median filter. Then the motion vectors are accumulated by motion estimation, in order to get denser and prominent motion vectors. Finally the moving objects are segmented adaptively by particle swarm clustering algorithm. It is demonstrated by the experimental results that the moving objects in the compressed domain can be segmented effectively.

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 CAMERA MOVING OBJECT SEGMENTATION IN COMPRESSED VIDEO SEQUENCES

2009 ◽  
Vol 09 (04) ◽  
pp. 609-627 ◽  
Author(s):  
J. WANG ◽  
N. V. PATEL ◽  
W. I. GROSKY ◽  
F. FOTOUHI
Keyword(s):  
Motion Detection ◽  
Object Segmentation ◽  
Object Motion ◽  
Moving Object ◽  
Transformation Model ◽  
Video Sequences ◽  
Compressed Domain ◽  
Camera Motion ◽  
Consistency Check ◽  
Moving Object Segmentation

In this paper, we address the problem of camera and object motion detection in the compressed domain. The estimation of camera motion and the moving object segmentation have been widely stated in a variety of context for video analysis, due to their capabilities of providing essential clues for interpreting the high-level semantics of video sequences. A novel compressed domain motion estimation and segmentation scheme is presented and applied in this paper. MPEG-2 compressed domain information, namely Motion Vectors (MV) and Discrete Cosine Transform (DCT) coefficients, is filtered and manipulated to obtain a dense and reliable Motion Vector Field (MVF) over consecutive frames. An iterative segmentation scheme based upon the generalized affine transformation model is exploited to effect the global camera motion detection. The foreground spatiotemporal objects are separated from the background using the temporal consistency check to the output of the iterative segmentation. This consistency check process can coalesce the resulting foreground blocks and weed out unqualified blocks. Illustrative examples are provided to demonstrate the efficacy of the proposed approach.

Moving Object Segmentation Based on Fusion-PCNN in Compressed Domain

2012 ◽  
Vol 41 (8) ◽  
pp. 914-921
Author(s):  
王慧斌 WANG Hui-bin ◽  
沈俊雷 SHEN Jun-lei ◽  
王鑫 WANG Xin ◽  
张丽丽 ZHANG Li-li
Keyword(s):  
Object Segmentation ◽  
Moving Object ◽  
Compressed Domain ◽  
Moving Object Segmentation

scholarly journals Accurate moving object segmentation in unconstraint videos based on robust seed pixels selection

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094727
Author(s):  
Wenlong Zhang ◽  
Xiaoliang Sun ◽  
Qifeng Yu
Keyword(s):  
Optical Flow ◽  
Moving Objects ◽  
Object Segmentation ◽  
Moving Object ◽  
Data Sets ◽  
Seed Selection ◽  
Current Frame ◽  
Moving Object Segmentation ◽  
Random Walker ◽  
Previous Frame

Due to the clutter background motion, accurate moving object segmentation in unconstrained videos remains a significant open problem, especially for the slow-moving object. This article proposes an accurate moving object segmentation method based on robust seed selection. The seed pixels of the object and background are selected robustly by using the optical flow cues. Firstly, this article detects the moving object’s rough contour according to the local difference in the weighted orientation cues of the optical flow. Then, the detected rough contour is used to guide the object and the background seed pixel selection. The object seed pixels in the previous frame are propagated to the current frame according to the optical flow to improve the robustness of the seed selection. Finally, we adopt the random walker algorithm to segment the moving object accurately according to the selected seed pixels. Experiments on publicly available data sets indicate that the proposed method shows excellent performance in segmenting moving objects accurately in unconstraint videos.

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,

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