Various systems and methods for counting people. For example, one method involves receiving input data at an analytics system that includes a neural network. The input data includes a representation of an environment, including representations of several people. The method also includes identifying the representations of the people in the representation of the environment. The method also includes updating an output value that indicates the number of people identified as being present in the environment.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving input data at a processor of an analytics system, wherein the input data comprises a representation of an environment at a first point in time, the representation of the environment comprises a plurality of representations of persons, and the analytics system comprises an artificial neural network; identifying, using the processor, a representation of a person of the plurality of representations; and updating an output value, wherein the output value indicates a number of persons identified as being present within the input data. 2. The method of claim 1, further comprising: selecting an output routine based on the output value; and performing the output routine. 3. The method of claim 1, further comprising: automatically compensating for a plurality of occlusions, wherein the input data comprises a visual representation of the environment, and the visual representation comprises the plurality of occlusions. 4. The method of claim 1, further comprising: converging sensor information from a plurality of input sources to form the input data, wherein the sensor information comprises image data and location information received from one or more wireless devices. 5. The method of claim 1, wherein the identifying comprises generating a feature vector using a generative process, and the feature vector comprises a sparse distributed representation of the input data. 6. The method of claim 5, wherein the generating comprises performing a discriminative process on the feature vector. 7. The method of claim 1, further comprising: generating an augmented data set, wherein the generating the augmented data set comprises injecting one or more modifications into a ground truth data set, and the one or more modifications are selected by a domain expert. 8. The method of claim 7, further comprising: generating a semantic fingerprint that comprises a probability distribution for the output value 9. The method of claim 8, further comprising: performing one or more pre-training operations of the analytics system, wherein the pre-training operations utilize the augmented data set and the semantic fingerprint. 10. A system comprising: a memory; and a processor coupled to the memory and configured to: receive input data at the processor, wherein the input data comprises a representation of an environment at a first point in time, the representation of the environment comprises a plurality of representations of persons, and the system comprises an artificial neural network; identify, using the processor, a representation of a person of the plurality of representations; and update an output value, wherein the output value indicates a number of persons identified as being present within the input data. 11. The system of claim 10, wherein the processor is further configured to: select an output routine based on the output value; and perform the output routine. 12. The system of claim 10, wherein the processor is further configured to: automatically compensate for a plurality of occlusions, wherein the input data comprises a visual representation of the environment, and the visual representation comprises the plurality of occlusions. 13. The system of claim 10, wherein the processor is further configured to: converge sensor information from a plurality of input sources to form the input data, wherein the sensor information comprises image data and location information received from one or more wireless devices. 14. The system of claim 10, wherein identifying the representation of the person comprises generating a feature vector using a generative process, and the feature vector comprises a sparse distributed representation of the input data. 15. The system of claim 14, wherein the generating comprises performing a discriminative process on the feature vector. 16. The system of claim 10, wherein the processor is further configured to: generate an augmented data set, wherein generating the augmented data set comprises injecting one or more modifications into a ground truth data set, and the one or more modifications are selected by a domain expert. 17. The system of claim 10, wherein the processor is further configured to: generate a semantic fingerprint that comprises a probability distribution for the output value 18. The system of claim 10, wherein the processor is further configured to: perform one or more pre-training operations of the analytics system, wherein the pre-training operations utilize the augmented data set and the semantic fingerprint. 19. A non-transitory computer readable storage medium comprising program instructions executable to: receive input data at a processor of an analytics system, wherein the input data comprises a representation of an environment at a first point in time, the representation of the environment comprises a plurality of representations of persons, and the system comprises an artificial neural network; identify, using the processor, a representation of a person of the plurality of representations; and update an output value, wherein the output value indicates a number of persons identified as being present within the input data. 20. The non-transitory computer readable storage medium of claim 19, wherein the program instructions are further executable to: select an output routine based on the output value; and perform the output routine.