Data Mining Practical Machine Learning Tools and Techniques

Background subtraction is a widely used approach for detecting moving objects from static cameras. Many different methods have been proposed over the recent years and both the novice and the expert can be confused about their benefits and limitations. In order to overcome this problem, this paper provides a review of the main methods and an original categorisation based on speed, memory requirements and accuracy. Such a review can effectively guide the designer to select the most suitable method for a given application in a principled way. Methods reviewed include parametric and non-parametric background density estimates and spatial correlation approaches.

/pdf/background-subtraction-techniques-a-review-13jzs7gesy.pdf

Background subtraction techniques: a review

This paper presents a technique for motion detection that incorporates several innovative mechanisms. For example, our proposed technique stores, for each pixel, a set of values taken in the past at the same location or in the neighborhood. It then compares this set to the current pixel value in order to determine whether that pixel belongs to the background, and adapts the model by choosing randomly which values to substitute from the background model. This approach differs from those based upon the classical belief that the oldest values should be replaced first. Finally, when the pixel is found to be part of the background, its value is propagated into the background model of a neighboring pixel. We describe our method in full details (including pseudo-code and the parameter values used) and compare it to other background subtraction techniques. Efficiency figures show that our method outperforms recent and proven state-of-the-art methods in terms of both computation speed and detection rate. We also analyze the performance of a downscaled version of our algorithm to the absolute minimum of one comparison and one byte of memory per pixel. It appears that even such a simplified version of our algorithm performs better than mainstream techniques.

/pdf/vibe-a-universal-background-subtraction-algorithm-for-video-3hbi5ckurx.pdf

ViBe: A Universal Background Subtraction Algorithm for Video Sequences

https://www.cin.ufpe.br/~rps/Artigos/Face%20Detection%20-%20%20A%20Survey.pdf

Face Detection

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.

/pdf/background-and-foreground-modeling-using-nonparametric-3hd5hi168b.pdf

Background and foreground modeling using nonparametric kernel density estimation for visual surveillance

This paper presents a novel background subtraction method for detecting foreground objects in dynamic scenes involving swaying trees and fluttering flags. Most methods proposed so far adjust the permissible range of the background image variations according to the training samples of background images. Thus, the detection sensitivity decreases at those pixels having wide permissible ranges. If we can narrow the ranges by analyzing input images, the detection sensitivity can be improved. For this narrowing, we employ the property that image variations at neighboring image blocks have strong correlation, also known as "cooccurrence". This approach is essentially different from chronological background image updating or morphological postprocessing. Experimental results for real images demonstrate the effectiveness of our method.

/pdf/background-subtraction-based-on-cooccurrence-of-image-1air38r10a.pdf

Background subtraction based on cooccurrence of image variations

In this paper, we describe a novel camera calibration method to estimate the extrinsic parameters and the focal length of a camera by using only one single image of two coplanar circles with arbitrary radius.

/pdf/camera-calibration-with-two-arbitrary-coplanar-circles-3539rr4w9d.pdf

Camera Calibration with Two Arbitrary Coplanar Circles

We address the problem of recovering the 3D shape of an unfolded book surface from the shading information in a scanner image. This shape-from-shading problem in a real world environment is made difficult by a proximal, moving light source, interreflections, specular reflections, and a nonuniform albedo distribution. Taking all these factors into account, we formulate the problem as an iterative, non-linear optimization problem. Piecewise polynomial models of the 3D shape and albedo distribution are introduced to efficiently and stably compute the shape in practice. Finally, we propose a method to restore the distorted scanner image based on the reconstructed 3D shape. The image restoration experiments for real book surfaces demonstrate that much of the geometric and photometric distortions are removed by our method.

Shape from Shading with Interreflections Under a Proximal Light Source: Distortion-Free Copying of an UnfoldedBook

Silhouette volume intersection is one of the most popular ideas for reconstructing the 3D volume of an object from multi-viewpoint silhouette images. This paper presents a novel parallel volume intersection method based on plane-to-plane homography for real-time 3D volume reconstruction using active cameras. This paper mainly focuses on the acceleration of back-projection from silhouette images to 3D space without using any sophisticated software technique, such as octree volume representation, or look-up table based projection acceleration. Also this paper presents a parallel intersection method of projected silhouette images. From the preliminary experimental results we estimate near frame-rate volume reconstruction for a life-sized mannequin can be achieved at 3 cm spatial resolution on our PC cluster system.

/pdf/homography-based-parallel-volume-intersection-toward-real-3mdmenb0gd.pdf

Homography based parallel volume intersection: toward real-time volume reconstruction using active cameras

Background subtraction is a simple and effective method to detect anomalous regions in images. In spite of the effectiveness, it cannot be used with an active (moving) camera head because the background image varies with camera-parameter control. This paper presents a background subtraction method with pan-tilt-zoom control. The proposed method consists of an omnidirectional background model called appearance sphere and parallax free sensing. Based on this model, precise background images can be generated and background subtraction can be performed for any combination of pan-tilt-zoom parameters without restoring 3D scene information.

/pdf/appearance-sphere-background-model-for-pan-tilt-zoom-camera-323ywhxo1t.pdf

Toshikazu Wada

Papers

Background subtraction based on cooccurrence of image variations

Camera Calibration with Two Arbitrary Coplanar Circles

Shape from Shading with Interreflections Under a Proximal Light Source: Distortion-Free Copying of an UnfoldedBook

Homography based parallel volume intersection: toward real-time volume reconstruction using active cameras

Appearance sphere: background model for pan-tilt-zoom camera