Siemens Corporate Research, Inc.

Siemens Corporation

Siemens Schweiz Ag

Imad Zoghlami

Visvanathan Ramesh

Vasudev Parameswaran

Dorin Comaniciu

Nikolaos Paragios

Anurag Mittal

Nikolaos Paragyios

Lan Dong

A method for performing crowd segmentation includes receiving video image data (S). Background differencing is performed on the received video image data to identify a foreground silhouette shape (S). Approximate number and position of human subjects within the received video image data are determined by matching the foreground silhouette shape against a set of predetermined foreground silhouette shapes, for each of which a number and position of human subjects is known (S). The estimated number and position of the human subjects is refined to determine a final number and position of the human subjects (S).

Fast crowd segmentation using shape indexing

A tunable representation for tracking that simultaneously encodes appearance and geometry in a manner that enables the use of mean-shift iterations for tracking is provided. The solution to the tracking problem is articulated into a method that encodes the spatial configuration of features along with their density and yet retains robustness to spatial deformations and feature density variations. The method of encoding of spatial configuration is provided using a set of kernels whose parameters can be optimized for a given class of objects off-line. The method enables the use of mean-shift iterations and runs in real-time. Better tracking results by the novel tracking method as compared to the original mean-shift tracker are demonstrated.

Tunable kernels for tracking

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 detecting events in a video sequence includes providing a video sequence, sampling the video sequence at regular intervals to form a series of snapshots of the sequence, measuring a similarity of each snapshot, measuring a similarity change between successive pairs of snapshots, wherein if a similarity change magnitude is greater than a predetermined threshold, a change event has been detected, verifying the change event to exclude a false positive, and completing the processing of the snapshot incorporating the verified change event.

Method and system for searching and verifying magnitude change events in video surveillance

A system and method for illumination invariant change detection are provided, the system including a processor, an energy ranking unit in signal communication with the processor for extracting block coefficients for the first and second images and computing an energy difference responsive to the coefficients for a frequency energy between the first and second images, and a change detection unit in signal communication with the processor for analyzing the energy difference and detecting a scene change if the energy difference is indicative of change; and the method including receiving first and second images, extracting block coefficients corresponding to frequency energies for the first and second images, computing an energy difference for at least one of the frequency energies between the first and second images, analyzing the at least one energy difference, and detecting a scene change if the energy difference is indicative of change.

Growing community of inventors

Plainsboro, NJ, United States of America

Imad Zoghlami