1,177 research outputs found
Spatiotemporal Saliency Detection: State of Art
Saliency detection has become a very prominent subject for research in recent time. Many techniques has been defined for the saliency detection.In this paper number of techniques has been explained that include the saliency detection from the year 2000 to 2015, almost every technique has been included.all the methods are explained briefly including their advantages and disadvantages. Comparison between various techniques has been done. With the help of table which includes authors name,paper name,year,techniques,algorithms and challenges. A comparison between levels of acceptance rates and accuracy levels are made
Human Motion Detection Based on Dual-Graph and Weighted Nuclear Norm Regularizations
Motion detection has been widely used in many applications, such as
surveillance and robotics. Due to the presence of the static background, a
motion video can be decomposed into a low-rank background and a sparse
foreground. Many regularization techniques that preserve low-rankness of
matrices can therefore be imposed on the background. In the meanwhile,
geometry-based regularizations, such as graph regularizations, can be imposed
on the foreground. Recently, weighted regularization techniques including the
weighted nuclear norm regularization have been proposed in the image processing
community to promote adaptive sparsity while achieving efficient performance.
In this paper, we propose a robust dual graph regularized moving object
detection model based on a novel weighted nuclear norm regularization and
spatiotemporal graph Laplacians. Numerical experiments on realistic human
motion data sets have demonstrated the effectiveness and robustness of this
approach in separating moving objects from background, and the enormous
potential in robotic applications.Comment: arXiv admin note: substantial text overlap with arXiv:2204.1193
Video Saliency Detection Using Object Proposals
In this paper, we introduce a novel approach to identify salient object regions in videos via object proposals. The core idea is to solve the saliency detection problem by ranking and selecting the salient proposals based on object-level saliency cues. Object proposals offer a more complete and high-level representation, which naturally caters to the needs of salient object detection. As well as introducing this novel solution for video salient object detection, we reorganize various discriminative saliency cues and traditional saliency assumptions on object proposals. With object candidates, a proposal ranking and voting scheme, based on various object-level saliency cues, is designed to screen out nonsalient parts, select salient object regions, and to infer an initial saliency estimate. Then a saliency optimization process that considers temporal consistency and appearance differences between salient and nonsalient regions is used to refine the initial saliency estimates. Our experiments on public datasets (SegTrackV2, Freiburg-Berkeley Motion Segmentation Dataset, and Densely Annotated Video Segmentation) validate the effectiveness, and the proposed method produces significant improvements over state-of-the-art algorithms
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