Abstract: 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.