Siemens Corporate Research, Inc.

Siemens Corporation

Anurag Mittal

Visvanathan Ramesh

Nikolaos Paragios

Dorin Comaniciu

Benedicte Bascle

Larry Davis

Ser-Nam Lim

Antoine Monnet

Imad Zoghlami

A method is disclosed for the analysis of scenarios where there are dynamically occurring objects capable of occluding each others. Application of the method in vision systems is also disclosed. Methods for incorporating visibility constraints for occluding scenarios are provided in a multi-camera setting. Other static constraints such as image resolution and field-of-view, and algorithmic requirements such as stereo reconstruction, face detection and background appearance are also addressed. A generic framework for sensor planning is also provided.

Generalized multi-sensor planning and systems

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.

Multiple camera system for obtaining high resolution images of objects

A multi-camera system that collects images and videos of moving objects in dynamic and crowded scenes, subject to task constraints is disclosed. The system constructs 'task visibility intervals' comprising information about what can be sensed in future time intervals. Methods for constructing these intervals applying prediction of future object motion and including consideration of factors such as object occlusion and camera control parameters are also disclosed. Using a plane-sweep algorithm, these atomic intervals can be combined in a method to form multi-task intervals, during which a single camera can collect videos suitable for multiple tasks simultaneously. Methods for fast camera scheduling that yield solutions within a small constant factor of an optimal solution are also disclosed.

Smart task-driven video collection

The present invention comprises using error propagation for building feature spaces with variable uncertainty and using variable-bandwidth mean shift for the analysis of such spaces, to provide peak detection and space partitioning. The invention applies these techniques to construct and analyze Hough spaces for line and geometrical shape detection, as well as to detect objects that are represented by peaks in the Hough space. This invention can be further used for background modeling by taking into account the uncertainty of the transformed image color and uncertainty of the motion flow. Furthermore, the invention can be used to segment video data in invariant spaces, by propagating the uncertainty from the original space and using the variable-bandwidth mean shift to detect peaks. The invention can be used in a variety of applications such as medical, surveillance, monitoring, automotive, augmented reality, and inspection.

Error propogation and variable-bandwidth mean shift for feature space analysis

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.

Video-based encroachment detection

A method for dynamic scene modeling and change detection applicable to motion analysis utilizes optical flow for capturing and modeling the dynamics of the scene. Uncertainties in the measurements are evaluated and utilized in order to develop a robust representation of the scene in a higher dimensional space. In another embodiment, a dynamical model of the scene is developed that utilizes multiple past frames to predict the next frame. Incremental methods for updating the model are developed and, towards detection of events, a new measure is introduced that is based on a state-driven comparison between the prediction and the actual observation.

Method for scene modeling and change detection

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.

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

A method of tracking an object comprises providing a plurality of cameras, determining an image from each camera, and determining a common plane in the images. The method further comprises determining a parallax for scene points across the images, incorporating the parallax as a feature in a background model, and estimating a change in the scene using the background model. A camera can be a pan-tilt-zoom camera. A camera can be uncalibrated.

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Plainsboro, NJ, United States of America

Anurag Mittal