Abstract: Methods and apparatus to estimate demographics of a household are disclosed. An example method to determine demographics for non-panelist households includes calculating a first demographic constraint average and a second demographic constraint average based on a first demographic distribution of a first tuning event of a household and a second demographic distribution of a second tuning event of the household. The household is a non-panelist household. The example method also includes, based on the first demographic constraint average, determining a first likelihood of the household being associated with a first demographic constraint. The example method also includes, based on the second demographic constraint average, determining a second likelihood of the household being associated with a second demographic constraint. The example method also includes estimating a household characteristic of the household based on the first likelihood and the second likelihood.

Abstract: Methods and apparatus to solve for a minimum variance solution for a constrained weight problem in a scalable and memory efficient manner are disclosed.

Abstract: Methods and apparatus to utilize a minimum cross entropy to calculate granular data of a region based on another region for media audience measurement. An example method for calculating granular data of a region for media audience measurement includes determining, by executing first instructions via a processor, aggregate behavioral data associated with a measurement of a media audience of a target region; determining, by executing second instructions via the processor, aggregate demographics data of the target region; and determining, by executing third instructions via the processor, granular data of a source region. The example method includes calculating, by executing fourth instructions via the processor, granular data of the media audience of the target region by utilizing minimum cross entropy to apportion the aggregate demographics data and the aggregate behavioral data of the target region based on the granular data of the source region to determine.

Abstract: Methods and apparatus to determine engagement levels of audience members are disclosed. An example computer readable storage medium includes computer readable instructions that, when executed, cause a processor to at least analyze image data from a sensor to determine whether an environment in which media is to be presented by a first device includes a second device with an illuminated display, and when the environment includes the second device with the illuminated display, determine whether the illuminated display of the second device is associated with a media presentation by the second device.

Abstract: Methods and apparatus to improve detection and false alarm rate over image segmentation are disclosed. An example method includes determining a first score based on a first pixel distance between a first feature of a first object in a reference image and a second feature in a second image of a search area, determining a second score corresponding to a second pixel distance between the second feature and a mathematical representation of a plurality of shapes representing a similarity between the second feature in the second image of the search area and the plurality of shapes observed simultaneously. The method further includes determining a normalized score by normalizing the first score based on the second score and identifying a second object in the second image of the search area as the first object in the reference image when the normalized score satisfies a threshold score.

Abstract: Methods and apparatus to solve for a minimum variance solution for a constrained weight problem in a scalable and memory efficient manner are disclosed. An example method of reducing memory usage of a computing device disclosed herein comprises storing a constraint matrix and a population matrix into a memory, the population matrix representing a population related to data of the constraint matrix, determining, via a processor, a linear system based on a set of constraints including the constraint matrix and the population matrix, determining, via a processor, an inverse of a matrix of the linear system, the determination of the inverse including breaking down the matrix into blocks, the dimensions of the blocks being no larger than dimensions of the constraint matrix, and outputting a set of weights based on the inverse of the linear system, the set of weights satisfying the set of constraints.

Abstract: Methods and apparatus to adjust media impressions based on media impression notification loss rates in network communications are disclosed.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to estimate demographics of users employing social media. An example method disclosed herein includes (1) identifying a social media message regarding an asset, the social media message associated with a user identifier associated with the user, (2) determining demographics associated with a group of people exposed to the asset, (3) associating the user identifier with the asset, and (4) repeating (1) to (3). The example method also includes (5) combining demographics associated with two or more different groups of people with which the user identifier is associated to estimate a demographic profile for the user.

Abstract: Methods and apparatus to determine engagement levels of audience members are disclosed. An example method includes capturing, with a sensor, image data depicting an environment in which a first media device is to present media. Analyzing the image data to determine whether the environment includes a second media device emanating a glow. When the environment includes the second media device emanating the glow, calculating an engagement for a person in the environment with respect to the first media device, the engagement being calculated based on a characteristic of the second media device emanating the glow.

Abstract: Methods and apparatus to estimate demographics of a household are disclosed. An example method to determine demographics for non-panelist households includes calculating a first demographic constraint average and a second demographic constraint average based on a first demographic distribution of a first tuning event of a household and a second demographic distribution of a second tuning event of the household. The household is a non-panelist household. The example method also includes, based on the first demographic constraint average, determining a first likelihood of the household being associated with a first demographic constraint. The example method also includes, based on the second demographic constraint average, determining a second likelihood of the household being associated with a second demographic constraint. The example method also includes estimating a household characteristic of the household based on the first likelihood and the second likelihood.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to identify co-relationships between media using social media. An example apparatus includes an audience estimator to: estimate a first audience of first media based on a first set of media-exposure social media messages corresponding to client devices referencing the first media, and estimate a second audience of second media based on a second set of media-exposure social media messages corresponding to the client devices referencing the second media.

Abstract: Methods, apparatus, and articles of manufacture to detect shapes are disclosed. Example methods disclosed herein include determining a first likelihood of detection for a reference shape at a first distance from an image capturing device, and storing the reference shape in a first group of a plurality of groups in the database based on a comparison of the first likelihood of detection with a first threshold, the first group associated with the first distance. Disclosed examples also include selecting reference shapes of the first group to compare to an object outline detected in an image in response to a query, the query including a depth value associated with the detected object outline, the selecting of the reference shapes of the first group based on the depth value.

Abstract: Methods and apparatus to utilize a minimum cross entropy to calculate granular data of a region based on another region for media audience measurement. An example method for calculating granular data of a region for media audience measurement includes determining, by executing first instructions via a processor, aggregate behavioral data associated with a measurement of a media audience of a target region; determining, by executing second instructions via the processor, aggregate demographics data of the target region; and determining, by executing third instructions via the processor, granular data of a source region. The example method includes calculating, by executing fourth instructions via the processor, granular data of the media audience of the target region by utilizing minimum cross entropy to apportion the aggregate demographics data and the aggregate behavioral data of the target region based on the granular data of the source region to determine.

Abstract: Methods and apparatus to apply household-level weights to audience measurement data at a household-member level are disclosed. An example method to determine demographics of populations to measure media audiences of populations includes determining demographics for members of a first household of a sub-population. First demographics of a first member of the first household are different than second demographics of a second member of the first household. The example method includes calculating a first household-level weight for the first household based on a demographics distribution of the sub-population and aggregate demographics of a population.

Abstract: Methods and apparatus to partition data are disclosed. An example apparatus to partition panelist data includes an indicator matrix generator to determine an indicator matrix including panelist vectors for panelists based on panelist data associated with the panelists, a first one of the panelist vectors for a first one of the panelists to indicate whether the first one of the panelists: has a first characteristic, meets a first criterion based on the first characteristic and a second characteristic, has a third characteristic, and meets a second criterion based on the third characteristic and a fourth characteristic. The example apparatus further includes a matrix reducer to reduce the indicator matrix to a set of unique panelist vectors that represent partitions of the panelist data, the partitions of the panelist data to utilize less storage capacity than the panelist data.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to identify co-relationships between media using social media. An example method includes obtaining media-exposure social media messages based on keywords and posting timestamps, and identifying a media-pairing including first media and second media. The example method also includes estimating a first audience of the first media based on a first set of media-exposure social media messages referencing the first media, and estimating a second audience of the second media based on a second set of media-exposure social media messages referencing the second media.

Abstract: Methods and apparatus to determine engagement levels of audience members are disclosed. An example method includes capturing, with a sensor, image data depicting an environment in which a first media device is to present media. Analyzing the image data to determine whether the environment includes a second media device emanating a glow. When the environment includes the second media device emanating the glow, calculating an engagement for a person in the environment with respect to the first media device, the engagement being calculated based on a characteristic of the second media device emanating the glow.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to determine a duration of media presentation based on tuning session duration. Example apparatus include a tuning session determiner to determine a first tuning session based on a period of time between channel changes of a first media presentation device; a presentation session determiner to determine first presentation session data within the determined first tuning session; a modeler to determine a model by relating the first tuning session with the first presentation session data; a receiver to determine a second tuning session for tuning data from a second media presentation device; and a presentation session estimator to: select the model for the second tuning session, based on a match of a first duration of the second tuning session and a second duration associated with the model, and estimate second presentation session data for the second tuning session based on the model.

Abstract: Methods and apparatus to monitor environments are disclosed. Example audience measurement devices disclosed herein include means for executing a first recognition analysis on three-dimensional data representative of a first object within a threshold distance from a three-dimensional sensor. Disclosed example audience measurement devices also include means for executing a second recognition analysis on two-dimensional data representative of a second object outside the threshold distance from the three-dimensional sensor. Disclosed example audience measurement devices further include means for combining a first detection of a first person provided by the first recognition analysis and a second detection of a second person provided by the second recognition analysis to generate a people count for an environment.

Abstract: Methods and apparatus to apply household-level weights to audience measurement data at a household-member level are disclosed. An example method to determine demographics of populations to measure media audiences of populations includes determining demographics for members of a first household of a sub-population. First demographics of a first member of the first household are different than second demographics of a second member of the first household. The example method includes calculating a first household-level weight for the first household based on a demographics distribution of the sub-population and aggregate demographics of a population.