2,287 research outputs found
Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation
Object detection is a fundamental step for automated video analysis in many
vision applications. Object detection in a video is usually performed by object
detectors or background subtraction techniques. Often, an object detector
requires manually labeled examples to train a binary classifier, while
background subtraction needs a training sequence that contains no objects to
build a background model. To automate the analysis, object detection without a
separate training phase becomes a critical task. People have tried to tackle
this task by using motion information. But existing motion-based methods are
usually limited when coping with complex scenarios such as nonrigid motion and
dynamic background. In this paper, we show that above challenges can be
addressed in a unified framework named DEtecting Contiguous Outliers in the
LOw-rank Representation (DECOLOR). This formulation integrates object detection
and background learning into a single process of optimization, which can be
solved by an alternating algorithm efficiently. We explain the relations
between DECOLOR and other sparsity-based methods. Experiments on both simulated
data and real sequences demonstrate that DECOLOR outperforms the
state-of-the-art approaches and it can work effectively on a wide range of
complex scenarios.Comment: 30 page
A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain
Detecting camouflaged moving foreground objects has been known to be
difficult due to the similarity between the foreground objects and the
background. Conventional methods cannot distinguish the foreground from
background due to the small differences between them and thus suffer from
under-detection of the camouflaged foreground objects. In this paper, we
present a fusion framework to address this problem in the wavelet domain. We
first show that the small differences in the image domain can be highlighted in
certain wavelet bands. Then the likelihood of each wavelet coefficient being
foreground is estimated by formulating foreground and background models for
each wavelet band. The proposed framework effectively aggregates the
likelihoods from different wavelet bands based on the characteristics of the
wavelet transform. Experimental results demonstrated that the proposed method
significantly outperformed existing methods in detecting camouflaged foreground
objects. Specifically, the average F-measure for the proposed algorithm was
0.87, compared to 0.71 to 0.8 for the other state-of-the-art methods.Comment: 13 pages, accepted by IEEE TI
- …