Artificial intelligence based motion detection
Methods and systems for motion detection are provided. Aspects includes receiving, from a sensor, sensor data associated with an area proximate to the sensor, determining an event type based on a feature vector, utilizing a machine learning model, the feature vector comprising a plurality of features extracted from the sensor data, and generating an alert based on the event type.
1. A system for motion detection, the system comprising:
a sensor;
a controller coupled to a memory, the controller configured to:
receive, from the sensor, sensor data associated with an area proximate to the sensor;
utilize a machine learning model to determine an event type based on a feature vector, the feature vector comprising a plurality of features extracted from the sensor data; and
generate an alert based on the event type;
wherein the sensor comprises an infrared sensor;
wherein the event type comprises a true alarm event and a false alarm event.
2. The system of claim 1 , wherein the true alarm event comprises a signal generated by a human movement in the area proximate to the sensor.
3. The system of claim 1 , wherein the false alarm event comprises a signal generated by sources other than a human movement.
4. The system of claim 1 , wherein the machine learning model is tuned with labeled training data; and
wherein the labeled training data comprises historical motion event data.
5. The system of claim 1 , wherein the plurality of features comprise characteristics of the signal generated by the sensor.
6. The system of claim 5 , wherein the characteristics of the signal comprise at least one of a vector rotation, a maximum, a minimum, an average, a magnitude deviation from an average, a number of empty cells in a vector data table, a ratio of amplitudes, a ratio of signals integrals, a number of signal samples and a shape factor.
7. The system of claim 1 , wherein the sensor comprises a passive infrared sensor.
8. The system of claim 1 , wherein generating the alert based on the event type comprises:
setting an output to an alarm based on a classification by the machine learning model as the true alarm event.
9. A method for motion detection, the method comprising:
receiving, from a sensor, sensor data associated with an area proximate to the sensor;
utilizing a machine learning model to determine an event type based on a feature vector, the feature vector comprising a plurality of features extracted from the sensor data; and
generating an alert based on the event type;
wherein the sensor comprises an infrared sensor;
wherein the event type comprises a true alarm event and a false alarm event.
10. The method of claim 9 , wherein the true alarm event comprises a signal generated by a human movement in the area proximate to the sensor.
11. The method of claim 9 , wherein the false alarm event comprises a signal generated by sources other than a human movement.
12. The method of claim 9 , wherein the machine learning model is tuned with labeled training data.
13. The method of claim 12 , wherein the labeled training data comprises historical motion event data.
14. The method of claim 9 , wherein the sensor data comprises a signal generated by the sensor.
15. The method of claim 9 , wherein the plurality of features comprise characteristics of the signal generated by the sensor.
16. The method of claim 15 , wherein the characteristics of the signal comprise at least one of a vector rotation, a maximum, a minimum, an average, a magnitude deviation from an average, a number of empty cells in a vector data table, a ratio of amplitudes, a ratio of signals integrals, a number of signal samples and a shape factor.