Providing assistance based on machine learning model analysis of video data

1 . A method comprising:

receiving, by one or more processors, video data of an environment that includes a liquid substance;

detecting, by the one or more processors using a machine learning model, in a first frame of the video data, a first individual within a distance threshold from the liquid substance;

detecting, by the one or more processors using the machine learning model, in a second frame of the video data, the first individual in the liquid substance based at least partially on analyzing the video data;

determining, by the one or more processors, a measure of confidence indicative of a likelihood of the machine learning model accurately detecting that the first individual is in the liquid substance;

analyzing, by the one or more processors using the machine learning model, a frame of the video data to determine a behavior of one or more additional individuals;

determining, by the one or more processors, one or more confidence factors based on at least the behavior;

increasing, by the one or more processors, the measure of confidence using the one or more confidence factors to determine a modified measure of confidence, wherein the behavior indicates that the one or more additional individuals are attempting to provide assistance to the first individual;

comparing, by the one or more processors, the modified measure of confidence to a confidence threshold; and

communicating assistance to be provided to the first individual to at least one of a client device or an assistance device in response to determining that the modified measure of confidence satisfies the confidence threshold.

2 . The method of claim 1 , wherein the video data comprises first video data from a first camera and second video data from a second camera;

wherein detecting the first individual further comprises analyzing, by the one or more processors using the machine learning model, the first video data to detect that the first individual is in the liquid substance prior to determining the measure of confidence; and

wherein determining the one or more confidence factors comprises analyzing, by the one or more processors using the machine learning model, the second video data to determine the one or more confidence factors.

3 . The method of claim 1 , wherein determining the one or more confidence factors comprises:

analyzing, by the one or more processors using the machine learning model, the first frame of the video data to determine a first quantity of individuals located within the distance threshold of the liquid substance, the first quantity of individuals including the first individual;

analyzing, by the one or more processors using the machine learning model, the second frame of the video data to determine a second quantity of individuals located within the distance threshold of the liquid substance, the second quantity of individuals excluding the first individual;

determining, by the one or more processors, the one or more confidence factors further based on a difference between the first quantity and the second quantity; and

increasing, by the one or more processors, the measure of confidence based on the one or more confidence factors to determine the modified measure of confidence, wherein the first quantity is greater than the second quantity.

4 . The method of claim 1 , wherein determining the one or more confidence factors comprises:

analyzing, by the one or more processors using the machine learning model, a first frame of the video data to identify an article of clothing that is in possession of the first individual prior to determining the measure of confidence;

analyzing, by the one or more processors using the machine learning model, a second frame of the video data to determine that the article of clothing is located at least partially on or in the liquid substance;

determining, by the one or more processors, the one or more confidence factors further based on determining that the article of clothing is located at least partially on or in the liquid substance; and

increasing, by the one or more processors, the measure of confidence based on the one or more confidence factors to determine the modified measure of confidence.

5 . The method of claim 1 , further comprising providing assistance to the first individual, wherein providing assistance to the first individual comprises:

determining, by the one or more processors, an estimated location of the first individual in the liquid substance; and

deploying, by the one or more processors, the assistance device to the estimated location.

6 . The method of claim 1 , wherein:

the client device is a device of a second individual that monitors the environment; and

communicating assistance to be provided to the first individual comprises providing a notification to the client device.

7 . A system, comprising:

one or more cameras to capture video data of an environment including a liquid substance; and

a guest assistance platform comprising a machine learning model, a response engine, and one or more processors, configured to:

receive the video data from the one or more cameras;

analyze, using the machine learning model, the video data;

detect, using the machine learning model, that a first individual is in the liquid substance;

determine a measure of confidence indicative of a likelihood of the machine learning model accurately detecting that the first individual has fallen into the liquid substance;

analyze, using the machine learning model, a frame of the video data to determine a behavior of one or more additional individuals in response to determining the measure of confidence;

determine one or more confidence factors associated with the measure of confidence based on at least the behavior;

increase the measure of confidence, using the one or more confidence factors, to determine a modified measure of confidence, wherein the behavior indicates that the one or more additional individuals are attempting to provide assistance to the first individual;

perform, by the response engine, a first action in response to determining that the modified measure of confidence is a first value that satisfies a first confidence threshold; and

perform, by the response engine, a second action different than the first action in response to determining that the modified measure of confidence is a second value different than the first value and the second value satisfies a second confidence threshold;

wherein the first action and the second action provide assistance to the first individual.

8 . The system of claim 7 , wherein the one or more processors are further configured to:

perform, by the response engine, a third action different than the first action and the second action in response to determining that the modified measure of confidence is a third value different than the first value and the second value and the third value satisfies a third confidence threshold, wherein the third action provides assistance to the first individual.

9 . The system of claim 7 , wherein the one or more processors are further configured to:

analyze, using the machine learning model, a frame of the video data to determine a behavior of the first individual prior to determining the measure of confidence;

determine the one or more confidence factors further further based on the behavior of the first individual; and

increase the measure of confidence based on the one or more confidence factors.

10 . The system of claim 7 , wherein the one or more processors are further configured to:

analyze, using the machine learning model, a first frame of the video data to detect an article of clothing that is in possession of the first individual;

analyze, using the machine learning model, a second frame of the video data to determine that the article of clothing is located at least partially on or within the liquid substance;

determine the one or more confidence factors further based on determining that the article of clothing is located at least partially on or within the liquid substance; and

increase the measure of confidence based on the one or more confidence factors to determine the modified measure of confidence.

11 . The system of claim 7 , wherein the one or more cameras capture audio data and the one or more processors are further configured to:

receive the audio data from the one or more cameras;

analyze, using the machine learning model, the audio data;

determine that the audio data indicates that the first individual has fallen into the liquid substance;

determine the one or more confidence factors further based on determining that the audio data indicates that the first individual has fallen into the liquid substance; and

increase the measure of confidence based on the one or more confidence factors to determine the modified measure of confidence.

12 . The system of claim 7 , further comprising:

a client device; and

an assistance device;

wherein to perform the first action, the one or more processors are configured to generate a notification to the client device to indicate that assistance is to be provided to the first individual; and

to perform the second action, the one or more processors are configured to:

determine an estimated location of the first individual in the liquid substance; and

deploy the assistance device to the estimated location.

13 . A non-transitory computer-readable medium comprising:

one or more instructions that, when executed by one or more processors, cause the one or more processors to:

update, using training data, a machine learning model;

receive video data of an environment;

analyze, using the machine learning model, a first portion of the video data;

detect a first individual in the environment;

analyze, using the machine learning model, a second portion of the video data;

determine that the first individual is missing from the environment in the second portion of the video data;

determine a measure of confidence indicative of a likelihood of the machine learning model accurately detecting that the first individual is missing from the environment by determining at least one of conditions of the environment, obstruction in the video data, or an amount of the training data used to update the machine learning model;

analyze, using the machine learning model, a frame of the video data to determine a behavior of one or more additional individuals in response to determining the measure of confidence;

determine one or more confidence factors associated with the measure of confidence based on at least the behavior;

modify the measure of confidence using the one or more confidence factors to determine a modified measure of confidence, wherein the measure of confidence is increased in response to the behavior indicating that the one or more additional individuals are attempting to provide assistance to the first individual; and

generate a notification to a client device to cause assistance to be provided to the first individual in response to determining that the modified measure of confidence satisfies a first confidence threshold.

14 . The non-transitory computer-readable medium of claim 13 ,

wherein the one or more instructions cause the one or more processors to:

analyze, using the machine learning model, audio data obtained with the video data;

determine that the audio data indicates that the first individual is missing from the environment;

determine the one or more confidence factors further based on determining that the audio data indicates that the first individual is missing from the environment; and

increase the measure of confidence based on the one or more confidence factors to determine the modified measure of confidence.

15 . The non-transitory computer-readable medium of claim 13 , wherein the one or more instructions cause the one or more processors to:

determine a time associated with the video data;

determine the one or more confidence factors further based on the time associated with the video data; and

modify the measure of confidence based on the one or more confidence factors to determine the modified measure of confidence.

16 . The non-transitory computer-readable medium of claim 13 , wherein the one or more instructions cause the one or more processors to:

analyze, using the machine learning model, the first portion of the video data to detect an article of clothing that is in possession of the first individual;

analyze, using the machine learning model, the second portion of the video data to determine that the article of clothing is located at least partially in the environment after detecting that the first individual is missing from the environment;

determine the one or more confidence factors further based on determining that the article of clothing is located at least partially in the environment after detecting that the first individual is missing from the environment; and

increase the measure of confidence based on the one or more confidence factors to determine the modified measure of confidence.

17 . The non-transitory computer-readable medium of claim 13 , wherein the one or more instructions cause the one or more processors to:

determine an estimated location of the first individual in the environment; and

deploy an assistance device to the estimated location.