System and method for selecting features for identifying human activities in a human-computer interacting environment
First Claim
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1. A system for selecting one or more features to identify one or more human activities in a human-computer interacting environment, the system comprising:
- a processor;
a non-transitory memory coupled to the processor, wherein the processor is capable of executing a plurality of modules stored in the memory, and wherein the plurality of modules comprise;
a receiving module that receives skeleton points associated with one or more humans, wherein the one or more humans perform the one or more human activities, and wherein the skeleton points comprise a plurality of position coordinates of the one or more humans;
a computation module that;
calculates a data variation factor for the skeleton points, wherein the data variation factor identifies a variation between two or more of the plurality of position coordinates of the one or more human, and wherein the data variation factor is indicative of a variation between the one or more humans performing one or more activities and a variation between the one or more human activities;
scales the data variation factor with respect to a maximum and a minimum value and sorts the data variation factor by distance metrics in a descending order such that the skeleton point is independent of the one or more human; and
computes the distance metrics by scaling first four maximum values, wherein first four maximum values correspond to five position coordinates from amongst the plurality of position coordinates;
a selection module that selects a set of skeleton points from the skeleton points based on the scaled data variation factor;
a feature defining module that;
identifies a change in position coordinates associated with the set of skeleton points by using one or more statistical parameters, wherein the set of skeleton points defines the one or more human activity to be identified; and
extracts one or more features from the set of skeleton points based on the change in the position coordinates; and
an identification module to identify the one or more human activities based on the extracted one or more features.
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Abstract
A System and method for identifying one or more human activities in a human-computer interacting environment. Skeleton points associated with a human are received. A data variation factor for the skeleton points is calculated, and a set of skeleton points is selected based on the data variation factor. One or more features are defined from the set of skeleton points by identifying a variance in coordinates of the set of skeleton points by using one or more statistical parameters. The one or more features are used to identify the one or more human activities.
27 Citations
13 Claims
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1. A system for selecting one or more features to identify one or more human activities in a human-computer interacting environment, the system comprising:
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a processor; a non-transitory memory coupled to the processor, wherein the processor is capable of executing a plurality of modules stored in the memory, and wherein the plurality of modules comprise; a receiving module that receives skeleton points associated with one or more humans, wherein the one or more humans perform the one or more human activities, and wherein the skeleton points comprise a plurality of position coordinates of the one or more humans; a computation module that; calculates a data variation factor for the skeleton points, wherein the data variation factor identifies a variation between two or more of the plurality of position coordinates of the one or more human, and wherein the data variation factor is indicative of a variation between the one or more humans performing one or more activities and a variation between the one or more human activities; scales the data variation factor with respect to a maximum and a minimum value and sorts the data variation factor by distance metrics in a descending order such that the skeleton point is independent of the one or more human; and computes the distance metrics by scaling first four maximum values, wherein first four maximum values correspond to five position coordinates from amongst the plurality of position coordinates; a selection module that selects a set of skeleton points from the skeleton points based on the scaled data variation factor; a feature defining module that; identifies a change in position coordinates associated with the set of skeleton points by using one or more statistical parameters, wherein the set of skeleton points defines the one or more human activity to be identified; and extracts one or more features from the set of skeleton points based on the change in the position coordinates; and an identification module to identify the one or more human activities based on the extracted one or more features. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A method for selecting one or more features to identify one or more human activities in a human-computer interacting environment, the method comprising:
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receiving skeleton points associated with one or more humans, wherein the one or more humans performs the one or more human activities, and wherein the skeleton points comprise position coordinates of the one or more human; calculating a data variation factor for the skeleton points, wherein the data variation factor identifies a variation between the position coordinates of the one or more humans and wherein the data variation factor is indicative of a variation between the one or more humans performing one or more activities and a variation between the one or more human activities; scaling the data variation factor with respect to a maximum and a minimum value and sorts the data variation factor by distance metrics in a descending order such that the skeleton point is independent of the one or more human; computing the distance metrics by scaling first four maximum values, wherein first four maximum values correspond to five position coordinates from amongst the plurality of position coordinates; selecting a set of skeleton points from the skeleton points based on the scaled data variation factor; identifying a change in position coordinates associated with the set of skeleton points by using one or more statistical parameters, wherein the set of skeleton points defines one or more human activity to be identified; extracting one or more features from the set of skeleton points based on the change in the position coordinates; and identifying the one or more human activities by using the extracted one or more features. - View Dependent Claims (12)
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13. A non-transitory computer storage medium having embodied thereon a computer program for selecting one or more features to identify one or more human activities in a human-computer interacting environment, the method comprising:
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a program code for receiving skeleton points associated with one or more humans, wherein the one or more humans performs the one or more human activities, and wherein the skeleton points comprise position coordinates of the one or more human; a program code for calculating a data variation factor for the skeleton points, wherein the data variation factor identifies a variation between the position coordinates of one or more humans, and wherein the data variation factor is indicative of a variation between the one or more humans performing one or more activities and a variation between the one or more human activities; a program code for scaling the data variation factor with respect to a maximum and a minimum value and sorts the data variation factor by distance metrics in a descending order such that the skeleton point is independent of the one or more human; a program code for computing the distance metrics by scaling first four maximum values, wherein first four maximum values correspond to five position coordinates from amongst the plurality of position coordinates; a program code for selecting a set of skeleton points from the skeleton points based on the scaled data variation factor; a program code for identifying a change in the position coordinates associated with the set of skeleton points by using one or more statistical parameters, wherein the set of skeleton points defines the one or more human activities to be identified; a program code for extracting one or more features from the set of skeleton points based on the change in the position coordinates; and a program code for identifying the one or more human activities by using the extracted one or more features.
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Specification