DECOLOR for Moving Object Detection


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 the 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.


From top to bottom: original image, background, segmentation

More examples


Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation.
X. Zhou, C. Yang, W. Yu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2013.



Updated on May 25, 2016.
The newest version of the GCO toolbox is included to solve the compatibility issue with the new versions of MATLAB.
Only the mex file for Win64 is included. Please compile the GCO toolbox if you are using another system.

Posted in My Projects