Scalable deep learning video analytics
First Claim
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1. A method comprising:
- receiving training data, the training data including training video data representing video of a location in a quiescent state;
training a neural network using the training data to obtain a plurality of metrics;
receiving current data, the current data including current video data representing video of the location at a current time period;
generating a reconstruction error based on the plurality of metrics and the current video data; and
generating, in response to determining that the reconstruction error is greater than a threshold, a notification indicative of the location being in a non-quiescent state.
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Abstract
In one embodiment, a method includes receiving training data, the training data including training video data representing video of a location in a quiescent state, training a neural network using the training data to obtain a plurality of metrics, receiving current data, the current data including current video data representing video of the location at a current time period, generating a reconstruction error based on the plurality of metrics and the current video data in the embedded space, and generating, in response to determining that the reconstruction error is greater than a threshold, a notification indicative of the location being in a non-quiescent state.
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Citations
14 Claims
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1. A method comprising:
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receiving training data, the training data including training video data representing video of a location in a quiescent state; training a neural network using the training data to obtain a plurality of metrics; receiving current data, the current data including current video data representing video of the location at a current time period; generating a reconstruction error based on the plurality of metrics and the current video data; and generating, in response to determining that the reconstruction error is greater than a threshold, a notification indicative of the location being in a non-quiescent state. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A system comprising:
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one or more processors; and a non-transitory memory comprising instructions that when executed cause the one or more processors to perform operations comprising; receive training data, the training data including training video data representing video of a location in a quiescent state; train a neural network using the training data to obtain a plurality of metrics; receive current data, the current data including current video data representing video of the location at a current time period; generate a reconstruction error based on the plurality of metrics and the current video data; and generate, in response to determining that the reconstruction error is greater than a threshold, a notification indicative of the location being in a non-quiescent state. - View Dependent Claims (13, 14)
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Specification