System And Process For Detecting, Tracking And Counting Human Objects of Interest

Abstract

A system is disclosed that includes: at least one image capturing device at the entrance to obtain images; a reader device; and a processor for extracting objects of interest from the images and generating tracks for each object of interest, and for matching objects of interest with objects associated with RFID tags, and for counting the number of objects of interest associated with, and not associated with, particular RFID tags.

Description

CROSS-REFERENCE[0001]This application is a continuation of U.S. patent application Ser. No. 12 / 942,108 filed on Nov. 9, 2010, which claims the benefit of U.S. Provisional Patent Application No. 61 / 294,013 filed on Jan. 11, 2010, which is incorporated herein in its entirety.BACKGROUND[0002]1. Field of the Invention[0003]The present invention generally relates to the field of object detection, tracking, and counting. In specific, the present invention is a computer-implemented detection and tracking system and process for detecting and tracking human objects of interest that appear in camera images taken, for example, at an entrance or entrances to a facility, as well as counting the number of human objects of interest entering or exiting the facility for a given time period.[0004]2. Related Prior Art[0005]Traditionally, various methods for detecting and counting the passing of an object have been proposed. U.S. Pat. No. 7,161,482 describes an integrated electronic article surveillanc...

AI Technical Summary

Benefits of technology

The system provides precise, real-time tracking and counting of human objects, distinguishing between different directions of traffic and types of objects, while being unobtrusive and cost-effective, suitable for various environments including retail settings.

Problems solved by technology

The major problem with approaches using background differencing is the presence of background clutters, which negatively affect robustness and reliability of the system performance.

Another problem is that the background updating rate is hard to adjust in real applications.

1) object detections tend to drift from true locations of the objects, or get fixed to strong features in the background; and

2) the detections are prone to occlusion. Approaches using the contour tracking suffer from difficulty in overcoming degradation by intensity gradients in the background near contours of the objects. In addition, all the previously mentioned methods are susceptible to changes in lighting conditions, shadows, and sunlight.

Range background differencing suffers from the same problems such as background clutter, as the monocular background differencing approaches, and presents difficulty in differentiating between multiple closely positioned objects.

Despite all the prior art in this field, no invention has developed a technology that enables unobtrusive detection and tracking of moving human objects, requiring low budget and maintenance while providing precise traffic counting results with the ability to distinguish between incoming and outgoing traffic, moving and static objects, and between objects of different heights.

In addition, people counting systems typically create anonymous traffic counts.

In retail traffic monitoring, however, this may be insufficient.

In these circumstances, existing systems are unable to separately track and count employees and customers.

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