Perceptual associative memory for a neuro-linguistic behavior recognition system

1. A method, comprising:

receiving a plurality of video frames from a video source, the plurality of video frames including a representation of an object;

normalizing, via at least one processor, data in each video frame from the plurality of video frames to obtain normalized data for the plurality of video frames;

generating, via the at least one processor and based on the normalized data, a syntax for a neuro-linguistic model, the syntax including a stable model of phrases, the generating the syntax including generating a connected graph having a plurality of nodes, each node from the plurality of nodes representing one machine-readable word from a plurality of machine-readable words having an associated predefined maximum symbol combination length;

identifying, via the at least one processor, instances of at least one machine-readable phrase from the stable model of phrases and based on the connected graph and a phrase length limit;

calculating, via the at least one processor, an unusualness score for an observation of a first machine-readable phrase from the at least one machine-readable phrase identified in the connected graph; and

publishing, via the at least one processor, an alert associated with the observation of the first machine-readable phrase, the alert indicating an anomaly in the behavior of the object.

2. The computer-implemented method of claim 1 , wherein generating the syntax includes:

generating, via the at least one processor, a plurality of machine-readable symbols from the normalized data, each machine-readable symbol from the plurality of machine-readable symbols being associated with a distinct cluster of the normalized data, and

building a dictionary of machine-readable words based on an observed sequence of the plurality of machine-readable symbols.

3. The computer-implemented method of claim 1 , wherein generating the syntax includes:

evaluating, via the at least one processor, statistics for a plurality of combinations of machine-readable words co-occurring in the plurality of machine-readable words, the statistics including a frequency at which combinations of machine-readable words from the plurality of combinations of machine-readable words co-occur; and

updating, via the at least one processor, a model of combinations of machine-readable words based on the evaluated statistics, the model identifying statistically relevant observations of co-occurring machine-readable words.

4. The computer-implemented method of claim 1 , wherein the connected graph further includes a plurality of edges, each edge from the plurality of edges connecting the nodes representing a probabilistic measure of co-occurrence of pairs of statistically relevant words in the plurality of machine-readable words.

5. The computer-implemented method of claim 1 , wherein the connected graph further includes a plurality of edges, each edge from the plurality of edges weighted based on a statistical significance score between each pair of nodes from the plurality of nodes.

6. The computer-implemented method of claim 1 , further comprising:

receiving the plurality of machine-readable words; and

updating statistics of the co-occurring words based on the plurality of machine-readable words.

7. The computer-implemented method of claim 1 , wherein generating the syntax includes generating a dictionary of machine-readable words based on an observed-sequence of the machine-readable symbols, the method further comprising at least one of:

decreasing a statistical significance score of co-occurring machine-readable words, from the dictionary of machine-readable words, that are less frequently observed over time; or

increasing a statistical significance score of co-occurring machine-readable words, from the dictionary of machine-readable words, that are more frequently observed over time.

8. A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to:

receive a plurality of video frames from a video source, the plurality of video frames including a representation of an object;

normalize, via at least one processor, data in each video frame from the received plurality of video frames to obtain normalized data for the plurality of video frames;

generate, via the at least one processor and based on the normalized data, a syntax for a neuro-linguistic model, the syntax including a stable model of phrases, the instructions to generate the syntax including instructions to generate a connected graph having a plurality of nodes, each node from the plurality of nodes representing one machine-readable word from a plurality of machine-readable words having an associated predefined maximum symbol combination length;

identify, via the at least one processor, instances of at least one machine-readable phrase from the stable model of phrases and based on the connected graph and a phrase length limit;

calculate, via the at least one processor, an unusualness score for an observation of a first machine-readable phrase from the at least one machine-readable phrase identified in the connected graph; and

publish, via the at least one processor, an alert associated with the observation of the first machine-readable phrase, the alert indicating an anomaly in the behavior of the object.

9. The non-transitory computer-readable storage medium of claim 8 , wherein the instructions to generate the syntax include instructions to:

generate a plurality of machine-readable symbols from the normalized data, each machine-readable symbol from the plurality of machine-readable symbols being associated with a distinct cluster of the normalized data, and

build a dictionary of machine-readable words based on an observed sequence of the plurality of machine-readable symbols.

10. The non-transitory computer-readable storage medium of claim 8 , wherein the instructions to generate the syntax include instructions to:

evaluate statistics for a plurality of combinations of machine-readable words co-occurring in the plurality of machine-readable words, the statistics including a frequency at which combinations of machine-readable words from the plurality of combinations of machine-readable words co-occur; and

update a model of combinations of machine-readable words based on the evaluated statistics, the model identifying statistically relevant observations of co-occurring machine-readable words.

11. The non-transitory computer-readable storage medium of claim 8 , wherein the connected graph further includes a plurality of edges, each edge from the plurality of edges connecting the nodes representing a probabilistic measure of co-occurrence of pairs of statistically relevant words in the plurality of machine-readable words.

12. The non-transitory computer-readable storage medium of claim 8 , wherein the connected graph also includes a plurality of edges, each edge from the plurality of edges weighted based on a statistical significance score between each pair of nodes from the plurality of nodes.

13. The non-transitory computer-readable storage medium of claim 8 , further storing instructions that, when executed by the processor, cause the processor to:

receive the plurality of machine-readable words; and

update statistics of the co-occurring words based on the plurality of machine-readable words.

14. The non-transitory computer-readable storage medium of claim 8 , wherein the instructions to generate the syntax include instructions to generate a dictionary of machine-readable words based on an observed-sequence of the machine-readable symbols, the non-transitory computer-readable storage medium further storing instructions that, when executed by the processor, cause the processor to:

decrease a statistical significance score of co-occurring machine-readable words, from the dictionary of machine-readable words, that are less frequently observed over time; or

increase a statistical significance score of co-occurring machine-readable words, from the dictionary of machine-readable words, that are more frequently observed over time.

15. A system, comprising:

a processor; and

a memory storing processor-executable instructions that, when executed by the processor, cause the processor to:

receive a plurality of video frames from a video source, the plurality of video frames including a representation of an object;

normalize, via at least one processor, data in each video frame from the received plurality of video frames to obtain normalized data for the plurality of video frames;

generate, via the at least one processor and based on the normalized data, a syntax for a neuro-linguistic model, the syntax including a stable model of phrases, the instructions to generate the syntax including instructions to generate a connected graph having a plurality of nodes, each node from the plurality of nodes representing one machine-readable word from a plurality of machine-readable words having an associated predefined maximum symbol combination length;

identify, via the at least one processor, instances of at least one machine-readable phrase from the stable model of phrases and based on the connected graph and a phrase length limit;

calculate, via the at least one processor, an unusualness score for an observation of a first machine-readable phrase from the at least one machine-readable phrase identified in the connected graph; and

publish, via the at least one processor, an alert associated with the observation of the first machine-readable phrase, the alert indicating an anomaly in the behavior of the object.

16. The system of claim 15 , wherein the instructions to generate the syntax include instructions to:

generate a plurality of machine-readable symbols from the normalized data, each machine-readable symbol from the plurality of machine-readable symbols being associated with a distinct cluster of the normalized data, and

build a dictionary of machine-readable words based on an observed sequence of the plurality of machine-readable symbols.

17. The system of claim 15 , wherein the instructions to generate the syntax include instructions to:

evaluate statistics for a plurality of combinations of machine-readable words co-occurring in the plurality of machine-readable words, the statistics including a frequency at which combinations of machine-readable words from the plurality of combinations of machine-readable words co-occur; and

update a model of combinations of machine-readable words based on the evaluated statistics, the model identifying statistically relevant observations of co-occurring machine-readable words.

18. The system of claim 15 , wherein the connected graph further includes a plurality of edges, each edge from the plurality of edges connecting the nodes representing a probabilistic measure of co-occurrence of pairs of statistically relevant words in the plurality of machine-readable words.

19. The system of claim 15 , wherein the connected graph further includes a plurality of edges, each edge from the plurality of edges weighted based on a statistical significance score between each pair of nodes from the plurality of nodes.

20. The system of claim 15 , wherein the memory further stores instructions that, when executed by the processor, cause the processor to:

receive the plurality of machine-readable words; and

update statistics of the co-occurring words based on the plurality of machine-readable words.