Method and system for media audience measurement and spatial extrapolation based on site, display, crowd, and viewership characterization

Abstract

The present invention provides a comprehensive method to design an automatic media viewership measurement system, from the problem of sensor placement for an effective sampling of the viewership to the method of extrapolating spatially sampled viewership data. The system elements that affect the viewership—site, display, crowd, and audience—are identified first. The site-viewership analysis derives some of the crucial elements in determining an effective data sampling plan: visibility, occupancy, and viewership relevancy. The viewership sampling map is computed based on the visibility map, the occupancy map, and the viewership relevancy map; the viewership measurement sensors are placed so that the sensor coverage maximizes the viewership sampling map. The crowd-viewership analysis derives a model of the viewership in relation to the system parameters so that the viewership extrapolation can effectively adapt to the time-changing spatial distribution of the viewership; the step identifies crowd dynamics, and its invariant features as the crucial elements that extract the influence of the site, display, and the crowd to the temporal changes of viewership. The extrapolation map is formulated around these quantities, so that the site-wide viewership can be effectively estimated from the sampled viewership measurement.

Description

BACKGROUND OF THE INVENTION[0001]1. Field of the Invention[0002]The present invention is a system and method for designing a comprehensive media audience measurement platform starting from a site, display, and crowd characterization, to a data sampling planning and a data extrapolation method.[0003]2. Background of the Invention[0004]The role of digital media for advertisement in public spaces is becoming increasingly important. The task of measuring the degree of media exposure is also deemed as very important both as a guide to the equipment installation (equipment kind, position, size, and orientation) and as a rating for the content programming. As the number of such displays is growing, measuring the viewing behavior of the audience using human intervention can be very costly.[0005]Unlike the traditional broadcast media, the viewing typically occurs in public spaces where a very large number of unknown people can assemble to comprise an audience. It is therefore hard to take su...

AI Technical Summary

Benefits of technology

[0064]In another exemplary embodiment of the present invention, a dedicated set of sensors can be used to measure the crowd dynamics. The sensors can be ceiling mounted so that they look down and potentially provide more reliable estimates of the crowd dynamics. The sensor placement can follow the same principle as the placement of the viewership measurement sensors; the placement only needs to consider the occupancy map so that the arrangement of the sensors can achieve a maximum coverage of the crowd motion.

Problems solved by technology

As the number of such displays is growing, measuring the viewing behavior of the audience using human intervention can be very costly.

It is therefore hard to take surveys of the audience using traditional interviewing through telephone or mail methods, and on-site interviews can be both very costly and potentially highly biased.

However, due to the high initial installation cost, the sensor placement planning is extremely important.

While the equipments deliver consistent viewership measurements, they have limitations of measuring the view from their individual fixed positions (and orientations, in most cases).

However, relocating the equipment can affect the integrity of the data.

In a typical media display scenario, it is unrealistic, if not impossible, to detect and record all instances of viewership occurring in the site using these sensors.

The problem to date with utilizing a dense motion vector field is that the information contained in a dense motion field is too large to transmit.

There may be other constraints to consider, such as physical constraints, the cost for specific placement, and the requirement to hide the sensors from public view, etc.

Because each floor position can have potentially multiple directions that the crowd frequently travels, vector field representation is not general enough.

In real scenarios, the distribution of viewership is not uniform and, from our assumption, it changes according to the crowd dynamics.

Images