Systems and methods for identifying a unified entity from a plurality of discrete parts

1. A system comprising:

a camera that captures a sequence of images of a monitored region that includes a sub-region of interest; and

a processor that receives the sequence of images and processes the sequence of images using heuristics and rules of an artificial intelligence model to (1) identify a plurality of discrete parts that are associated with a type of a unified entity and (2) virtually link together a group of the plurality of discrete parts that correspond to a specific embodiment of the unified entity that is present in the sub-region of interest,

wherein the heuristics and the rules of the artificial intelligence model are developed from a training process that includes the artificial intelligence model receiving sample images delineating exemplary discrete parts on exemplary embodiments of the unified entity,

wherein the group of the plurality of discrete parts includes visible ones of the plurality of discrete parts and at least partially occluded ones of the plurality of discrete parts, and

wherein the processor uses the heuristics and the rules of the artificial intelligence model to identify the at least partially occluded ones of the plurality of discrete parts based on a respective type of each of the visible ones of the plurality of discrete parts, a respective location of each of the visible ones of the plurality of discrete parts, and locations of one or more obstacles within the sub-region of interest.

2. The system of claim 1 wherein the type of the unified entity includes a human, wherein the plurality of discrete parts includes individual body parts of the human, wherein the specific embodiment of the unified entity includes a specific person present in the sub-region of interest, wherein the exemplary discrete parts include exemplary body parts, and wherein the exemplary embodiments of the unified entity include one or more exemplary persons.

3. The system of claim 1 wherein the processor tracks the specific embodiment of the unified entity relative to one or more obstacles in the sub-region of interest by tracking movement of each of the group of the plurality of discrete parts.

4. The system of claim 3 wherein the processor uses the heuristics and the rules of the artificial intelligence model to determine whether the specific embodiment of the unified entity is at least partially occluded by the one or more obstacles, and wherein the training process includes the artificial intelligence model identifying the exemplary embodiments of the unified entity being at least partially occluded by the one or more obstacles.

5. The system of claim 3 wherein the one or more obstacles include an underwater area of the sub-region of interest.

6. The system of claim 1 wherein the heuristics and the rules of the artificial intelligence model virtually link together the group of the plurality of discrete parts based on extrapolating from exemplary groupings of the exemplary discrete parts delineated in the exemplary embodiments of the unified entity as identified in the sample images.

7. The system of claim 1 wherein the heuristics and the rules of the artificial intelligence model virtually link together the group of the plurality of discrete parts by identifying a respective type of each of the plurality of discrete parts, identifying a respective location of each of the plurality of discrete parts, and identifying each of the plurality of discrete parts for which the respective type and the respective location conform to a model of the unified entity developed from the training process.

8. The system of claim 1 wherein the processor uses the heuristics and the rules of the artificial intelligence model to determine whether movement of each of the group of the plurality of discrete parts is indicative of an emergency situation or an alarm situation, and wherein the training process includes the artificial intelligence model identifying positions of the exemplary discrete parts during the emergency situation or the alarm situation.

9. The system of claim 1 wherein the processor uses the heuristics and the rules of the artificial intelligence model to determine whether movement of each of the group of the plurality of discrete parts is indicative of unauthorized access to the sub-region of interest, and wherein the training process includes the artificial intelligence model identifying the unauthorized access to the sub-region of interest.

10. A method comprising:

capturing a sequence of images of a monitored region that includes a sub-region of interest;

processing the sequence of images using heuristics and rules of an artificial intelligence model to identify a plurality of discrete parts that are associated with a type of a unified entity;

using the heuristics and the rules of the artificial intelligence model to identify at least partially occluded ones of the plurality of discrete parts in the group of the plurality of discrete parts based on a respective type of each of visible ones of the plurality of discrete parts in the group of the plurality of discrete parts, a respective location of each of the visible ones of the plurality of discrete parts, and locations of one or more obstacles within the sub-region of interest; and

processing the sequence of images using the heuristics and the rules of the artificial intelligence model to virtually link together a group of the plurality of discrete parts that correspond to a specific embodiment of the unified entity that is present in the sub-region of interest,

wherein the heuristics and the rules of the artificial intelligence model are developed from a training process that includes the artificial intelligence model receiving sample images delineating exemplary discrete parts on exemplary embodiments of the unified entity.

11. The method of claim 10 wherein the type of the unified entity includes a human, wherein the plurality of discrete parts includes individual body parts of the human, wherein the specific embodiment of the unified entity includes a specific person present in the sub-region of interest, wherein the exemplary discrete parts include exemplary body parts, and wherein the exemplary embodiments of the unified entity include one or more exemplary persons.

12. The method of claim 10 further comprising:

tracking the specific embodiment of the unified entity relative to one or more obstacles in the sub-region of interest by tracking movement of each of the group of the plurality of discrete parts.

13. The method of claim 12 further comprising:

using the heuristics and the rules of the artificial intelligence model to determine whether the specific embodiment of the unified entity is at least partially occluded by the one or more obstacles,

wherein the training process includes the artificial intelligence model identifying the exemplary embodiments of the unified entity being at least partially occluded by the one or more obstacles.

14. The method of claim 12 wherein the one or more obstacles include an underwater area of the sub-region of interest.

15. The method of claim 10 further comprising:

using the heuristics and the rules of the artificial intelligence model to virtually link together the group of the plurality of discrete parts by extrapolating from exemplary groupings of the exemplary discrete parts delineated in the exemplary embodiments of the unified entity as identified in the sample images.

16. The method of claim 10 further comprising:

using the heuristics and the rules of the artificial intelligence model to virtually link together the group of the plurality of discrete parts by identifying a respective type of each of the plurality of discrete parts, identifying a respective location of each of the plurality of discrete parts, and identifying each of the plurality of discrete parts for which the respective type and the respective location conform to a model of the unified entity developed from the training process.

17. The method of claim 10 further comprising:

using the heuristics and the rules of the artificial intelligence model to determine whether movement of each of the group of the plurality of discrete parts is indicative of an emergency situation or an alarm situation,

wherein the training process includes the artificial intelligence model identifying positions of the exemplary discrete parts during the emergency situation or the alarm situation.

18. The method of claim 10 further comprising:

using the heuristics and the rules of the artificial intelligence model to determine whether movement of each of the group of the plurality of discrete parts is indicative of unauthorized access to the sub-region of interest,

wherein the training process includes the artificial intelligence model identifying the unauthorized access to the sub-region of interest.