| 摘要 |
The present invention provides a comprehensive method to design an automatic media audience measurement system that can estimate the site-wide audience of a media of interest (e.g., the site-wide viewership of a target display) based on the measurements of a subset of the actual audience sampled from a limited space in the site. This invention enables (1) the placement of sensors in optimal positions for the viewership data measurement and (2) the estimation of the site-wide viewership of the target display by performing the viewership extrapolation based on the sampled viewership data. The viewership extrapolation problem is formulated in a way that the time-varying crowd dynamics around the target display is an important decisive factor as well as the sampled viewership data at a given time in yielding the estimated site-wide viewership. To solve this problem, the system elements that affect the viewership—site, display, crowd, and audience—and their relationships are first identified in terms of the visibility, the viewership relevancy, and the crowd occupancy. The optimal positions of the sensors are determined to cover the maximum area of the viewership with high probabilities. The viewership extrapolation function is then modeled and learned from the sampled viewership data, the site-wide viewership data, and the crowd dynamics measurements while removing the noise in the sampled viewership data using the viewership relevancy of the measurements to the target display. |
| 主权项 |
1. A method for building a media audience measurement system based on a viewership extrapolation of measured media audience in a site, comprising the following steps of:
a) estimating, by a computer, viewership relevancy distribution and viewer occupancy distribution, b) estimating, by the computer, viewership distribution from both the viewership relevancy distribution and the viewer occupancy distribution, c) placing a plurality of sensors in optimal positions to measure viewership data in the site with given constraints that include budget and hardware specifications of available sensors, d) modeling features for capturing viewership and crowd dynamics, e) modeling functions for extrapolation-function learning, f) measuring, by the computer, a first set of sampled viewership data, a first set of site-wide viewership data, and a first set of crowd dynamics data, g) capturing a plurality of images using a plurality of sensors deployed in the site, h) dividing each image into multiple regions in a grid, i) extracting optical flows of each image from each region in the grid, j) constructing a histogram of the optical flows according to their orientations for each region in the grid, k) concatenating histograms of multiple regions, whereby the crowd dynamics data of an image can be represented by a single feature vector, l) estimating, by the computer, a first set of true sampled viewership data from the first set of sampled viewership data, m) learning viewership extrapolation functions based on relationship between the first set of sampled viewership data and the first set of site-wide viewership data while taking into account the first set of crowd dynamics data, and n) extrapolating, by the computer, a second set of site-wide viewership data using a second set of sampled viewership data and a second set of crowd dynamics data when the media audience measurement system is deployed in the site, wherein the viewership distribution is defined as probability distribution that a viewer is located at a certain position in a site and a viewer is engaged with a display. |
