Detecting contextual trends in digital video content
First Claim
1. A system, comprising:
- one or more computers; and
one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising;
detecting events shown within image frames of digital video content captured by one or more video cameras, the detected events being associated with corresponding event parameters and detection times within a first time period, wherein the video cameras are disposed at corresponding geographic locations and the detected events are associated with corresponding ones of the geographic locations;
applying a predictive model that includes a machine learning algorithm to values of the event parameters, the predictive model identifying a time-varying pattern in the detected events within the first time period;
based on an outcome of applying the predictive model to the values of the event parameters, determining a plurality of expected occurrences of additional events during a second time period, the additional events being associated with corresponding ones of the geographic locations and corresponding additional event parameters, and the second time period occurring after the first time period; and
transmitting, over a data communications network, data identifying at least a subset of the expected occurrences of the additional events to a communications device having a display, the communications device being configured to present a representation of each expected occurrence in the subset to a user through an interface of the display.
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Abstract
Computerized methods and systems, including computer programs encoded on a computer storage medium, may detect events shown within digital video content captured by one or more video cameras during a prior time period, and predict an occurrence of an additional event during a future time period based on time-varying patterns among the detected events. For example, a computing system may detect events shown within digital video content captured by one or more video cameras, and may establish a predictive model that identifies one or more time-varying patterns in event parameter values that characterize the detected events within the prior time period. Based on an outcome of the predictive model, the computing system may determine an expected value of one of the event parameters during the second time period.
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Citations
18 Claims
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1. A system, comprising:
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one or more computers; and one or more storage devices storing instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising; detecting events shown within image frames of digital video content captured by one or more video cameras, the detected events being associated with corresponding event parameters and detection times within a first time period, wherein the video cameras are disposed at corresponding geographic locations and the detected events are associated with corresponding ones of the geographic locations; applying a predictive model that includes a machine learning algorithm to values of the event parameters, the predictive model identifying a time-varying pattern in the detected events within the first time period; based on an outcome of applying the predictive model to the values of the event parameters, determining a plurality of expected occurrences of additional events during a second time period, the additional events being associated with corresponding ones of the geographic locations and corresponding additional event parameters, and the second time period occurring after the first time period; and transmitting, over a data communications network, data identifying at least a subset of the expected occurrences of the additional events to a communications device having a display, the communications device being configured to present a representation of each expected occurrence in the subset to a user through an interface of the display. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A computer-implemented method, comprising:
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detecting, by at least one processor, events shown within image frames of digital video content captured by one or more video cameras, the detected events being associated with corresponding event parameters and detection times within a first time period, wherein the video cameras are disposed at corresponding geographic locations and the detected events are associated with corresponding ones of the geographic locations; applying, by the at least one processor, a predictive model that includes a machine learning algorithm to values of the event parameters, the predictive model identifying a time-varying pattern in the detected events within the first time period; based on an outcome of applying the predictive model to the values of the event parameters, determining, by the at least one processor, a plurality of expected occurrences of additional events during a second time period, the additional events being associated with corresponding ones of the geographic locations and corresponding additional event parameters, and the second time period occurring after the first time period; and transmitting, by the at least one processor and over a data communications network, data identifying at least a subset of the expected occurrences of the additional events to a communications device having a display, the communications device configured to present a representation of each expected occurrence in the subset to a user through an interface of the display. - View Dependent Claims (12, 13, 14, 15, 16, 17)
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18. One or more non-transitory computer-readable media storing instructions that, when executed by at least one processor of a client device, cause performance of operations comprising:
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detecting events shown within image frames of digital video content captured by one or more video cameras, the detected events being associated with corresponding event parameters and detection times within a first time period, wherein the video cameras are disposed at corresponding geographic locations and the detected events are associated with corresponding ones of the geographic locations; applying a predictive model that includes a machine learning algorithm to values of the event parameters, the predictive model identifying a time-varying pattern in the detected events within the first time period; based on an outcome of applying the predictive model to the values of the event parameters, determining a plurality of expected occurrences of additional events during a second time period, the additional events being associated with corresponding ones of the geographic locations and corresponding additional event parameters, and the second time period occurring after the first time period; and transmitting, over a data communications network, data identifying at least a subset of the expected occurrences of the additional events to a communications device having a display, the communications device configured to present a representation of each expected occurrence in the subset to a user through an interface of the display.
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Specification