Method and system for automatic objects localization

Abstract

A method for automatic localization of objects in a mask. The method includes building a dictionary or atoms, wherein each atom models the presence of one object at one location and iteratively determining the atom of said dictionary which is best correlated with said mask, until ending criteria are met. The invention system concerns also automatically detects objects in a mask. At least one fixed camera is provided for acquiring video frames. A computation device is used for calibrating at least one fixed camera for extracting foreground silhouettes in each acquired video frames for discretizing said ground plane into a non-regular grid of potential location points for constructing a dictionary of atoms, and for finding objects location points with the previous method. And a propagating device is provided to propagate the result in at least one fixed camera view.

Description

RELATED APPLICATIONS[0001]This application is a continuation of U.S. patent application Ser. No. 12 / 779,547, filed May 13, 2010, the disclosure of which is hereby incorporated by reference herein.FIELD OF THE INVENTION[0002]The present invention concerns a method and a system for automatic localization and / or detection and / or identification and / or singulation and / or segmentation and / or computation and / or tracking of objects. More precisely, objects are automatically detected and localized on the ground and in the image planes of a network of cameras or even a single camera setup. Objects can be people in a crowded environment.DESCRIPTION OF RELATED ART[0003]Accurate vision-based people detection has been of interest for the past decades in many applications. A number of surveillance applications for example require the detection of people to ensure security, safety and site management. Examples include the behavior analysis and the monitoring of entry points, bus terminal and train ...

AI Technical Summary

Benefits of technology

The present invention provides a method and system for detecting and locating objects that are robust against noise, occlusions, and sudden changes in the scene. This system is scalable and can be used with any number of cameras, regardless of their type or network. It does not require a large amount of memory storage.

Problems solved by technology

The main difficulty in automatic localization of the objects comes from the mutual occlusions of these objects in a group.

The most effective techniques in the related art for detecting moving people or objects occluding each other are highly complex and may thus require the use of costly hardware.

Moreover, they may not be fast enough to perform in real-time detection and localization of people or objects within a crowd.

This method does not work by using a single camera with fixed orientation.

Such an approach is computationally intensive.

Moreover, the higher the accuracy of representation of the foreground silhouette shape to be matched, the more complex the calculations are.

Current approaches fail to robustly isolate different persons in the image of a group of people taken by one camera due to their mutual occlusions.

However, current multi-view approaches do not robustly segment a crowd of people.

This mathematical framework leads to potentially high false positives rate.

In addition, the computation cost of the algorithm depends on the number of ground plane points to be evaluated, which limits the area to be monitored.

Moreover, this algorithm does not work in real-time.

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