The exponentially increasing use of moving platforms for video capture introduces the urgent need to develop the general background subtraction algorithms with the capability to deal with the moving background. In this paper, we propose a multilayer-based framework for online background subtraction for videos captured by moving cameras. Unlike the previous treatments of the problem, the proposed method is not restricted to binary segmentation of background and foreground, but formulates it as a multi-label segmentation problem by modeling multiple foreground objects in different layers when they appear simultaneously in the scene. We assign an independent processing layer to each foreground object, as well as the background, where both motion and appearance models are estimated, and a probability map is inferred using a Bayesian filtering framework. Finally, Multi-label Graph-cut on Markov Random Field is employed to perform pixel-wise labeling. Extensive evaluation results show that the proposed method outperforms state-of-the-art methods on challenging video sequences.
Traditionally, statistical representations of the background scene have been proposed to estimate spatially extendable background models. Hayman al @cite_36 built a mixture of Gaussian mosaic background model. Ren al @cite_32 used motion compensation to predict the position of each pixel in a background map, and model the uncertainty of that prediction by a spacial Gaussian distribution. The construction of image mosaic associated with a traditional mixture Gaussian background model was also claimed to be effective in @cite_21 @cite_28 . However, the hyper-parameter required by this parametric model restricts its adaptability and application. On the contrary, we employ nonparametric Kernal Density Estimation method @cite_9 to build models of the appearance of foreground and background regions, making our approach more stable and applicable.
@cite_36 @cite_28 @cite_9 @cite_21 @cite_32
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Statistical background modelling and subtraction has proved to be a popular and effective class of algorithms for segmenting independently moving foreground objects out from a static background, without requiring any a priori information of the properties of foreground objects. We present two contributions on this topic, aimed towards robotics where an active head is mounted on a mobile vehicle. In periods when the vehicle's wheels are not driven, camera translation is virtually zero, and background subtraction techniques are applicable. This is also highly relevant to surveillance and video conferencing. The first part presents an efficient probabilistic framework for when the camera pans and tilts. A unified approach is developed for handling various sources of error, including motion blur, subpixel camera motion, mixed pixels at object boundaries, and also uncertainty in background stabilisation caused by noise, unmodelled radial distortion and small translations of the camera. The second contribution regards a Bayesian approach to specifically incorporate uncertainty concerning whether the background has yet been uncovered by moving foreground objects. This is an important requirement during initialisation of a system. We cannot assume that a background model is available in advance since that would involve storing models for each possible position, in every room, of the robot's operating environment.. Instead the background mode must be generated online, very possibly in the presence of moving objects. Abstract A method of robust feature-detection is proposed for visual tracking with a pan-tilt head. Even with good foreground models, the tracking process is liable to be disrupted by strong features in the background. Previous researchers have shown that the disruption can be somewhat suppressed by the use of image-subtraction. Building on this idea, a more powerful statistical model of background intensity is proposed in which a Gaussian mixture distribution is fitted to each of the pixels on a ‘virtual’ image plane. A fitting algorithm of the ‘Expectation-Maximisation’ type proves to be particularly effective here. Practical tests with contour tracking show marked improvement over image subtraction methods. Since the burden of computation is off-line, the online tracking process can run in real-time, at video field-rate. Automatic understanding of events happening at a site is the ultimate goal for many visual surveillance systems. Higher level understanding of events requires that certain lower level computer vision tasks be performed. These may include detection of unusual motion, tracking targets, labeling body parts, and understanding the interactions between people. To achieve many of these tasks, it is necessary to build representations of the appearance of objects in the scene. This paper focuses on two issues related to this problem. First, we construct a statistical representation of the scene background that supports sensitive detection of moving objects in the scene, but is robust to clutter arising out of natural scene variations. Second, we build statistical representations of the foreground regions (moving objects) that support their tracking and support occlusion reasoning. The probability density functions (pdfs) associated with the background and foreground are likely to vary from image to image and will not in general have a known parametric form. We accordingly utilize general nonparametric kernel density estimation techniques for building these statistical representations of the background and the foreground. These techniques estimate the pdf directly from the data without any assumptions about the underlying distributions. Example results from applications are presented. We present a method for modeling a scene that is observed by a moving camera, where only a portion of the scene is visible at any time. This method uses mixture models to represent pixels in a panoramic view, and to construct a "background image" that contains only static (non-moving) parts of the scene. The method can be used to reliably detect moving objects in a video sequence, detect patterns of activity over a wide field of view, and remove moving objects from a video or panoramic mosaic. The method also yields improved results in detecting moving objects and in constructing mosaics in the presence of moving objects, when compared with techniques that are not based on scene modeling. We present examples illustrating the results. A new background subtraction method is proposed in this paper for the foreground detection from a non-stationary background. Usually, motion compensation is required when applying background subtraction to a non-stationary background. In practice, it is difficult to realize this to sufficient pixel accuracy. The problem is further complicated when the moving objects to be detected tracked are small, since the pixel error in motion compensating the background will hide the small targets. A spatial distribution of Gaussians model is proposed to deal with moving object detection where the motion compensation is not exact but approximated. The distribution of each background pixel is temporally and spatially modeled. Based on this statistical model, a pixel in the current frame is classified as belonging to the foreground or background. In addition, a new background restoration and adaptation algorithm is developed for the non-stationary background over an extended period of time. Test cases involving a surveillance system to detect small moving objects (human and car) within a highly textured background and a pan-tilt human tracking system are demonstrated successfully.