MOTION DETECTION AND RECOGNITION EMPLOYING CONTEXTUAL AWARENESS

1 . A method, comprising:

receiving, by an application processor, a plurality of values from a corresponding plurality of heterogeneous sensors and audio/visual data corresponding to the detection of motion of an object located in the audio/visual data;

evaluating context, using an artificial intelligence engine executed by the application processor, of the plurality of values from the corresponding plurality of heterogeneous sensors and the audio/visual data in view of one or more past values from the plurality of sensors and one or more past frames of audio/visual data; and

triggering, by the artificial intelligence engine, in response to the evaluated context indicating that the motion of the object is suspicious with a probability equal to or above a level, an alert indicating that a suspicious event has occurred.

2 . The method of claim 1 , further comprising capturing the plurality of values and the audio/visual data over a period of time.

3 . The method of claim 2 , wherein the period of time corresponding to time before, during, and after the occurrence of the suspicious event.

4 . The method of claim 2 , wherein capturing the plurality of values and the audio/visual data over a period of time comprises creating one or more baseline scenarios against which potentially suspicious events are compared.

5 . The method of claim 1 , further comprising feeding the plurality of values and the audio/visual data into a self-learning method associated with the artificial intelligence method to improve on a conclusion made for a future suspicious event.

6 . The method of claim 5 , wherein the self-learning method correlates the plurality of values, the audio/visual data, and the indicated suspicious event in view of other sets of the plurality of values and the audio/visual data to determine whether certain events, taken in a context of overall data collected, serves as a trigger for a motion detection alert.

7 . The method of claim 1 , wherein the plurality of heterogeneous sensors comprise one or more of a camera, a microphone, a door sensor, a window sensor, a smoke detector, or another type of environmental particle detector.

8 . The method of claim 1 , wherein the data from the plurality of heterogeneous sensors and the audio/visual data are received by the processing device over a corresponding plurality of wireless communication channels.

9 . The method of claim 1 , wherein the plurality of sensors and a plurality of devices that capture the audio/visual data are distributed over a plurality of rooms in a building, and further comprising analyzing, by the artificial intelligence method, data generated by plurality of devices that capture the audio/visual data simultaneously and analyzing prior captured data to determine if motion detected in one room is consistent with non-suspicious behavior.

10 . The method of claim 1 , further comprising employing, by the artificial intelligence method, a facial recognition method to determine the difference between a human and other motion, as well as to learn which humans belong in a building and which humans are foreign to that building.

11 . The method of claim 1 , further comprising comparing a sound corresponding to the received audio data against a database of known sounds.

12 . The method of claim 1 , further comprising receiving, by the application processor, an indication from a user that the alert is accurate or inaccurate.

13 . The method of claim 12 , wherein receiving the indication that the alert is accurate or inaccurate comprises receiving a tag to associate with the audio/visual data as an aid for the artificial intelligence algorithm to use for detecting future motion detection events.

14 . The method of claim 1 , further comprising when an alert is categorized as accurate or inaccurate, transmitting, by the application processor to a central server, metadata associated with the received data for inclusion in a master database of alerts in order to help other unrelated devices improve their accuracy over time.

15 . The method of claim 1 , further comprising, prior to receiving the plurality of values from the corresponding plurality of heterogeneous sensors,

receiving, by the application processor, a plurality of preset values corresponding to the plurality of heterogeneous sensors and

training the artificial intelligence method with the plurality of preset values to determine events and alerts.

16 . The method of claim 1 , further comprising, storing, by the application processor in a memory, a log of each detected event to aid in the artificial intelligence method to render future detections of events.

17 . The method of claim 1 , further comprising:

employing a prediction engine to measure a response time of a user to one or more detected events; and

classifying a severity of each of the one or more events based on the response time.

18 . The method of claim 1 , wherein triggering an alert further comprises indicating a probable cause of the event.

19 . The method of claim 1 , wherein triggering an alert further comprises indicating one or more probabilities of the type of object that caused the motion.

20 . The method of claim 1 , further comprising, broadcasting, by the application processor, the received plurality of values to one or more other processing devices in a network of processing devices to aid in detection of events.

21 . The method of claim 1 , wherein one or more of the received plurality of values originate from one or more other processing devices in a network of application processors to aid in detection of events.

22 . A system comprising:

a memory;

an application processor, operatively coupled to the memory to:

receive a plurality of values from a corresponding plurality of heterogeneous sensors and audio/visual data corresponding to the detection of motion of an object located in the audio/visual data;

evaluate context, using an artificial intelligence engine, of the plurality of values from the corresponding plurality of heterogeneous sensors and the audio/visual data in view of one or more past values from the plurality of sensors and one or more past frames of audio/visual data; and

trigger, by the artificial intelligence engine, in response to the evaluated context indicating that the motion of the object is suspicious with a probability equal to or above a level, an alert indicating that a suspicious event has occurred.

23 . A non-transitory computer-readable medium storing instructions that when executed by an application processor, cause the application processor to:

receive a plurality of values from a corresponding plurality of heterogeneous sensors and audio/visual data corresponding to the detection of motion of an object located in the audio/visual data;

evaluate context, using an artificial intelligence engine, of the plurality of values from the corresponding plurality of heterogeneous sensors and the audio/visual data in view of one or more past values from the plurality of sensors and one or more past frames of audio/visual data; and

trigger, by the artificial intelligence engine, in response to the evaluated context indicating that the motion of the object is suspicious with a probability equal to or above a level, an alert indicating that a suspicious event has occurred.