1,014 research outputs found
Face Recognition with Contiguous Occlusion Using Markov Random Fields
Coordinated Science Laboratory was formerly known as Control Systems LaboratoryONR / N00014-09-1-0230National Science Foundation / NSF IIS 08-49292 and NSF ECCS 07-0167
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
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