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.
Many motion detection and tracking algorithms rely on the process of background subtraction, a technique which detects changes from a model of the background scene. We present a new algorithm for the purpose of background model initialization. The algorithm takes as input a video sequence in which moving objects are present, and outputs a statistical background model describing the static parts of the scene. Multiple hypotheses of the background value at each pixel are generated by locating periods of stable intensity in the sequence. The likelihood of each hypothesis is then evaluated using optical flow information from the neighborhood around the pixel, and the most likely hypothesis is chosen to represent the background. Our results are compared with those of several standard background modeling techniques using surveillance video of humans in indoor environments. In many visual tracking and surveillance systems, it is important to initialize a background model using a training video sequence which may include foreground objects. In such a case, robust statistical methods are required to handle random occurrences of foreground objects (i.e., outliers), as well as general image noise. The robust statistical method Median has been employed for initializing the background model. However, the Median can tolerate up to only 50 outliers, which cannot satisfy the requirements of some complicated environments. In this paper, we propose a novel robust method for the background initialization. The proposed method can tolerate more than 50 of foreground pixels and noise. We give quantitative evaluations on a number of video sequences and compare our proposed method with five other methods. Experiments show that our method can achieve very promising results in background initialization: including applications in video segmentation, visual tracking and surveillance.