Logical sensor generation in a behavioral recognition system
View Patent ↗Techniques are disclosed for generating logical sensors for an image driver. The image driver monitors values corresponding to at least a first feature in one or more regions of a first image in a stream of images received by a first sensor. The image driver identifies at least a first correlation between at least a first and second value of the monitored values. The image driver generates a logical sensor based on the identified correlations. The logical sensor samples one or more features corresponding to the identified correlation from a second image in the stream of images.
1. A computer-implemented method comprising:
receiving image data for an image;
extracting a feature value associated with a first feature of the image data;
generating a feature vector based on the feature value;
generating a sample vector based on the feature vector, the sample vector representing a low-dimensional representation of the image data;
monitoring subsequent feature values for the first feature;
detecting a correlation between the subsequent feature values for the first feature and feature values for a second feature; and
generating a logical sensor based on the correlation,
the first feature including one of a homogeneity, a shape, a motion activity, or an entropy of the image data.
2. The computer-implemented method of claim 1 , further comprising sending the sample vector to a server for incorporation into a learning model of the server.
3. The computer-implemented method of claim 1 , further comprising extracting a plurality of feature values including the feature value, each feature value from the plurality of feature values being associated with a region of the image.
4. The computer-implemented method of claim 3 , wherein the generating the feature vector is based on the plurality of feature values.
5. The computer-implemented method of claim 1 , wherein the generating the sample vector includes concatenating a plurality of feature vectors including the feature vector.
6. The computer-implemented method of claim 1 , wherein the method is performed by an image driver.
7. A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, perform an operation comprising:
receiving image data for an image;
extracting a feature value associated with a first feature of the image data;
generating a feature vector based on the feature value;
generating a sample vector based on the feature vector, the sample vector representing a low-dimensional representation of the image data;
monitoring subsequent feature values for the first feature;
detecting a correlation between the subsequent feature values for the first feature and feature values for a second feature; and
generating a logical sensor based on the correlation,
the first feature including one of a homogeneity, a shape, a motion activity, or an entropy of the image data.
8. The computer-readable storage medium of claim 7 , wherein the operation further comprises sending the sample vector to a server for incorporation into a learning model of the server.
9. The computer-readable storage medium of claim 7 , wherein the operation further comprises extracting a plurality of feature values including the feature value, each feature value from the plurality of feature values being associated with a region of the image.
10. The computer-readable storage medium of claim 7 , wherein the generating the sample vector includes concatenating a plurality of feature vectors including the feature vector.
11. A system, comprising:
a processor; and
a memory storing code, which, when executed on the processor, performs an operation, comprising:
receiving image data for an image;
extracting a feature value associated with a first feature of the image data;
generating a feature vector based on the feature value;
generating a sample vector based on the feature vector, the sample vector representing a low-dimensional representation of the image data;
monitoring subsequent feature values for the first feature;
detecting a correlation between the subsequent feature values for the first feature and feature values for a second feature; and
generating a logical sensor based on the correlation,
the first feature including one of a homogeneity, a shape, a motion activity, or an entropy of the image data.
12. The system of claim 11 , wherein the operation further comprises sending the sample vector to a server for incorporation into a learning model of the server.
13. The system of claim 11 , wherein the operation further comprises extracting a plurality of feature values including the feature value, each feature value from the plurality of feature values being associated with a region of the image.
14. The system of claim 11 , wherein the generating the sample vector includes concatenating a plurality of feature vectors including the feature vector.