Systems and methods for training event prediction models for camera-based warning systems

A device, comprising:

• one or more processors configured to:
• receive image data and sensor data associated with the image data, wherein the image data depicts a series of scenes that are captured over a time period and that are associated with an occurrence of an event;
• determine a time of interest, within the time period, at which a value of the sensor data satisfies one or more criteria;
• extract a portion of the image data based on the time of interest;
• extract, from the portion of the image data, a first slice of image data depicting a first set of scenes, of the series of scenes, and a second slice of image data depicting a second set of scenes, of the series of scenes, wherein a start of the first slice of image data is after a beginning of the portion of interest, wherein an end of the second slice of image data is before an end of the portion of interest, wherein the time of interest is at least partially between an end of the first slice of image data and a beginning of the second slice of image data;
• and
• train, based on the first slice of image data and the second slice of image data, a model to predict a probability of an occurrence of an event and to determine whether the probability satisfies a probability threshold.

A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:

• one or more instructions that, when executed by one or more processors of a device, cause the device to:
• receive image data and sensor data associated with the image data, wherein the image data depicts a series of scenes that are captured over a time period and that are associated with an occurrence of an event;
• determine a time of interest, within the time period, at which a value of the sensor data satisfies one or more criteria;
• extract a portion of the image data based on the time of interest;
• extract, from the portion of the image data, a first slice of image data depicting a first set of scenes, of the series of scenes, and a second slice of image data depicting a second set of scenes, of the series of scenes, wherein a start of the first slice of image data is after a beginning of the portion of interest, wherein an end of the second slice of image data is before an end of the portion of interest, wherein the time of interest is at least partially between an end of the first slice of image data and a beginning of the second slice of image data;
• and
• train, based on the first slice of image data and the second slice of image data, a model to predict a probability of an occurrence of an event and to determine whether the probability satisfies a probability threshold.