scholarly journals Motion Segmentation by New Three-View Constraint from a Moving Camera

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Fuyuan Xu ◽  
Guohua Gu ◽  
Kan Ren ◽  
Weixian Qian

We propose a new method for the motion segmentation using a moving camera. The proposed method classifies each image pixel in the image sequence as the background or the motion regions by applying a novel three-view constraint called the “parallax-based multiplanar constraint.” This new three-view constraint, being the main contribution of this paper, is derived from the relative projective structure of two points in three different views and implemented within the “Plane + Parallax” framework. The parallax-based multiplanar constraint overcomes the problem of the previous geometry constraint and does not require the reference plane to be constant across multiple views. Unlike the epipolar constraint, the parallax-based multiplanar constraint modifies the surface degradation to the line degradation to detect the motion objects followed by a moving camera in the same direction. We evaluate the proposed method with several video sequences to demonstrate the effectiveness and robustness of the parallax-based multiplanar constraint.

2006 ◽  
Vol 21 (7) ◽  
pp. 550-561
Author(s):  
Valery Naranjo ◽  
Luis Vergara ◽  
Antonio Alcaraz ◽  
Jose M. Mossi ◽  
Antonio Albiol
Keyword(s):  

2007 ◽  
Vol 04 (03) ◽  
pp. 227-236 ◽  
Author(s):  
TAEHO KIM ◽  
KANG-HYUN JO

A background is a part that does not vary too much or change frequently in an image sequence. Using this assumption, an algorithm of reconstructing remained background and detecting moving objects for static and also moving camera is presented. For generating background, we detect regions that have high correlation coefficient compared within prior pyramid images from the current image. These detected regions are used for two process. First, we calculate the temporal displacement vector of each detected regions and classify clusters of pixel intensity based on camera movement. Second, we calculate temporally principal displacement vector using histogram of displacement vectors. Temporally principal displacement vector indicates camera movement. Finally we eliminate clusters which have lower weight than threshold, and combine remained clusters for each pixel to generate multiple background clusters. Experimental results show that remained background model and detected moving object under camera moving.


2015 ◽  
Vol 7 (0) ◽  
pp. 163-174 ◽  
Author(s):  
Xuefeng Liang ◽  
Cuicui Zhang ◽  
Takashi Matsuyama

Author(s):  
Mazouzi Amine ◽  
Kerfa Djoudi ◽  
Ismail Rakip Karas

<span lang="EN-US">In this article, a new method of vehicles detecting and tracking is presented: A thresholding followed by a mathematical morphology treatment are used. The tracking phase uses the information about a vehicle. An original labeling is proposed in this article. It helps to reduce some artefacts that occur at the detection level. The main contribution of this article lies in the possibility of merging information of low level (detection) and high level (tracking). In other words, it is shown that many artefacts resulting from image processing (low level) can be detected, and eliminated thanks to the information contained in the labeling (high level). The proposed method has been tested on many video sequences and examples are given illustrating the merits of our approach.</span>


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