Showing papers by "Anurag Mittal published in 2005"

Fast illumination-invariant background subtraction using two views: error analysis, sensor placement and applications

Abstract: Background modeling and subtraction to detect new or moving objects in a scene is an important component of many intelligent video applications. Compared to a single camera, the use of multiple cameras leads to better handling of shadows, specularities and illumination changes due to the utilization of geometric information. Although the result of stereo matching can be used as the feature for detection, it has been shown that the detection process can be made much faster by a simple subtraction of the intensities observed at stereo-generated conjugate pairs in the two views. The methodology however, suffers from false and missed detections due to some geometric considerations. In this paper, we perform a detailed analysis of such errors. Then, we propose a sensor configuration that eliminates false detections. Algorithms are also proposed that effectively eliminate most detection errors due to missed detections, specular reflections and objects being geometrically close to the background. Experiments on several scenes illustrate the utility and enhanced performance of the proposed approach compared to existing techniques.

System and method for fast illumination-invariant background subtraction using two views

Abstract: A method for eliminating errors in foreground object detection in digitized images comprises providing a reference camera and a secondary camera, vertically aligning each said camera with a baseline that is approximately perpendicular to a ground plane, wherein said reference camera is placed lower than said secondary camera, selecting a foreground pixel in a reference view of a first point in a foreground object, finding a conjugate pixel of the foreground pixel in a secondary view, using the foreground and conjugate pixels to determine an image base pixel of a base point in the reference view, wherein said base point is a point on the ground plane below the first point, and using the foreground and image base pixels to find a location where the ground plane is first visible.

Video-based encroachment detection

Abstract: A method and system for video-based encroachment detection are provided, the method including receiving first and second images, modeling a background from the first image, subtracting the background from the second image to provide a detection map, calibrating the size of an object from the pixel level, integrating a projection of the object with the detection map using dynamic programming, and detecting the object in a region if the projection matches that region of the detection map; and the system including a processor, a background modeling unit coupled with the processor for modeling a background from the first image and subtracting the background from the second image to provide a detection map, and a dynamic programming unit coupled with the processor for calibrating the size of an object from the pixel level, integrating a projection of the object with the detection map, and detecting the object in a region if the projection matches that region of the detection map.

Multiple camera system for obtaining high resolution images of objects

Abstract: A system and corresponding method for image acquisition are provided, the system including a processor, an imaging adapter in signal communication with the processor for receiving image data from each of a static imaging device and a dynamic imaging device, and a homography unit in signal communication with the processor for computing a planar homography between the static and dynamic image data; and the method including receiving an image from a static imaging device, receiving an image from a dynamic imaging device, and registering the dynamic image to the static image using planar homography.

Constructing task visibility intervals for a surveillance system

Abstract: One of the goals of a multi-camera surveillance system is to collect useful video clips of objects in the scene. Objects in the collected videos should be unobstructed, in the field of view of the given camera, and meet task-specific resolution requirement. For this purpose, we describe an algorithm that constructs "task visibility intervals", which are tuples of information about what to sense (task-object pairs), when to sense (feasible future temporal intervals to start a task) and how to sense (the camera to use and the corresponding viewing angles and focal length). The algorithm first looks for temporal intervals within which the angular extents of objects overlap each other, causing the object farthest from the given camera to be occluded. Outside these intervals, sub-intervals are then constructed such that feasible camera settings exist for capturing the object. Experimental results are provided to illustrate the system capabilities in constructing such task visibility intervals, followed by scheduling them using a greedy algorithm.