Method for monitoring occupancy in a work area

1. A method for monitoring occupancy in a work area comprising:

accessing a first image recorded by a sensor block at a first time;

detecting a first distribution of objects and a work surface in the first image;

detecting a quantity of humans and a cluster of objects within a distance threshold of the work surface in the first image;

in response to the quantity of humans falling below a human quantity threshold and in response to detecting the cluster of objects within the distance threshold of the work surface, updating an occupancy status of the work surface to indicate the work surface as occupied with humans absent;

accessing a second image recorded by the sensor block at a second time;

detecting a second distribution of objects, absence of humans, absence of clusters of objects, and the work surface present in the work area in the second image;

calculating an object entropy in the work area based on a first difference between the first distribution of objects and the second distribution of objects; and

in response to detecting absence of humans and detecting absence of clusters of objects and in response to the object entropy exceeding an entropy threshold, updating the occupancy status of the work surface to indicate the work surface as vacant.

2. The method of claim 1 , further comprising:

accessing a third image recorded by the sensor block at a third time;

detecting a third distribution of objects in the third image;

detecting a second quantity of humans within the distance threshold of the work surface in the third image;

updating the object entropy in the work area based on a second difference between the first distribution of objects, the second distribution of objects, and the third distribution of objects; and

in response to the second quantity of humans exceeding the human quantity threshold and in response to the object entropy falling below the entropy threshold, updating the occupancy status of the work surface to indicate the work surface as occupied with humans present.

3. The method of claim 2 :

further comprising at the first time:

calculating a target rate of air exchange in the work area proportional to the quantity of humans within the distance threshold of the work surface;

generating a first command indicating the target rate of air exchange in the work area; and

serving the first command to a ventilation control system coupled to the work area; and

further comprising at the third time:

updating the target rate of air exchange in the work area proportional to the second quantity of humans within the distance threshold of the work surface;

generating a second command indicating an increase in the target rate of air exchange in the work area; and

serving the second command to the ventilation control system coupled to the work area to update the target rate of air exchange.

4. The method of claim 1 :

further comprising at the first time, extracting a first set of features from the first image;

wherein detecting the first distribution of objects and the work surface in the first image comprises detecting the first distribution of objects and the work surface based on the first set of features;

wherein detecting the quantity of humans and the cluster of objects comprises detecting the quantity of humans and the cluster of objects within the distance threshold of the work surface based on the first set of features;

further comprising at the second time, extracting a second set of features from the second image; and

wherein detecting the second distribution of objects, absence of humans, absence of clusters of objects, and the work surface present in the work area comprises detecting the second distribution of objects, absence of humans, absence of clusters of objects, and the work surface present in the work area based on the second set of features.

5. The method of claim 1 , further comprising:

accessing a third image recorded by the sensor block at a third time;

extracting a third set of features from the third image;

based on the third set of features:

detecting the work surface present in the work area;

detecting a second cluster of objects within the distance threshold of the work surface; and

detecting absence of humans within the distance threshold of the work surface; and

in response to detecting the second cluster of objects within the distance threshold of the work surface and in response to detecting absence of humans:

updating the occupancy status of the work surface to indicate the work surface as occupied with humans absent; and

generating a notification to alert maintenance staff to clean the work area.

6. The method of claim 5 :

wherein detecting the second cluster of objects within the distance threshold of the work surface comprises detecting a quantity of human effects within the distance threshold of the work surface in the third image;

further comprising detecting a third distribution of objects in the third image;

further comprising updating the object entropy in the work area based on a second difference between the first distribution of objects, the second distribution of objects, and the third distribution of objects; and

further comprising in response to the quantity of human effects falling below an effect threshold and in response to the object entropy exceeding the entropy threshold:

maintaining the occupancy status of the work surface to indicate the work surface as vacant; and

updating the notification to alert maintenance staff to clean the work area.

7. The method of claim 1 :

wherein detecting the quantity of humans and the cluster of objects comprises detecting the quantity of humans and a quantity of human effects within the distance threshold of the work surface in the first image; and

wherein updating the occupancy status of the work surface to indicate the work surface as occupied with humans absent comprises updating the occupancy status of the work surface to indicate the work surface as occupied with humans absent in response to the quantity of humans falling below the human quantity threshold and in response to the quantity of human effects exceeding an effect threshold.

8. The method of claim 7 , wherein detecting the quantity of humans and the quantity of human effects within the distance threshold of the work surface, the quantity of human effects is selected from the group consisting of:

computing devices;

notebooks;

beverage containers;

articles of clothing; and

bags.

9. The method of claim 1 :

wherein accessing the first image recorded by the sensor block at the first time comprises accessing the first image recorded by the sensor block at the first time, the sensor block facing the work area comprising a reception area;

wherein detecting the first distribution of objects and the work surface in the first image comprises detecting the work surface comprising a table in the first image;

wherein detecting the quantity of humans and the cluster of objects comprises detecting the quantity of humans and the cluster of objects within the distance threshold of the table in the first image; and

wherein updating the occupancy status of the work surface comprises updating the occupancy status of the table to indicate the table as occupied with humans absent.

10. The method of claim 9 :

wherein updating the occupancy status of the work surface to indicate the work surface as vacant comprises updating the occupancy status of the table to indicate the table as vacant; and

further comprising generating a notification to alert maintenance staff to straighten the reception area.

11. The method of claim 1 , wherein detecting the second distribution of objects, absence of humans, absence of clusters of objects, and the work surface present in the work area in the second image comprises:

detecting a second quantity of humans within the threshold distance of the work surface;

detecting a first quantity of human effects within the threshold distance of the work surface;

detecting absence of humans in the work area in response to the second quantity of humans falling below the human quantity threshold; and

detecting absence of clusters of objects in the work area in response to the first quantity of human effects falling below an effect threshold.

12. The method of claim 1 :

wherein accessing the first image recorded by the sensor block at the first time comprises accessing the first image recorded by the sensor block at the first time, the sensor block facing the work area comprising a cafeteria;

wherein detecting the first distribution of objects and the work surface in the first image comprises detecting the work surface comprising a table in the first image;

wherein detecting the quantity of humans and the cluster of objects comprises detecting the quantity of humans and the cluster of objects within the distance threshold of the table in the first image; and

wherein updating the occupancy status of the work surface comprises updating the occupancy status of the table to indicate the table as occupied with humans absent.

13. The method of claim 1 , further comprising at the third time, in response to detecting absence of humans and absence of clusters of objects:

generating a lighting command to deactivate lights in the work area; and

serving the lighting command to a lighting control system coupled to the work area to update a lighting level in the work area.

14. The method of claim 1 , further comprising:

accessing a third image recorded by the sensor block at a third time;

detecting a second quantity of humans within the workspace in the third image; and

in response to the second quantity of humans exceeding an occupancy threshold for the workspace, generating a notification to dispatch support staff to the work area.

15. A method for monitoring occupancy in a work area comprising:

accessing a first image recorded by a sensor block at a first time, the sensor block facing the work area;

detecting a first distribution of objects in the first image;

extracting a first set of features from the first image;

based on the first set of features:

detecting a work surface present in the work area; and

detecting absence of humans and a cluster of objects within a distance threshold of the work surface;

in response to detecting absence of humans and in response to detecting the cluster of objects within the distance threshold of the work surface, updating an occupancy status of the work surface to indicate the work surface as occupied with humans absent;

accessing a second image recorded by the sensor block at a second time;

detecting a second distribution of objects in the second image;

extracting a second set of features from the second image:

detecting the work surface present in the work area; and

detecting absence of humans and absence of the cluster of objects within the threshold distance of the work surface;

calculating an object entropy in the work area based on a difference between the first distribution of objects and the second distribution of objects; and

in response to detecting absence of humans and absence of the cluster of objects within the threshold distance of the work surface and in response to the object entropy exceeding an entropy threshold:

updating the occupancy status of the work surface to indicate the work surface as vacant; and

generating a notification to alert maintenance staff to clean the work area.

16. The method of claim 15 :

further comprising, at the first time, detecting a first quantity of human effects within the distance threshold of the work surface from the first image;

further comprising for each human effect in the first quantity of human effects:

identifying a human effect type;

calculating a centroid of the human effect; and

associating the human effect with the work surface;

wherein detecting the quantity of humans and the cluster of objects within the distance threshold of the work surface comprises detecting a cluster of human effects associated with the work surface and within the distance threshold of the work surface; and

further comprising:

detecting a third quantity of humans based on the human effect type, the centroid of the human effect, and the cluster of human effects associated with the work surface; and

in response to the third quantity of humans falling below the human quantity threshold, maintaining the occupancy status of the work surface to indicate the work surface as occupied with humans absent.

17. The method of claim 16 , wherein detecting the first quantity of human effects within the threshold distance of the work surface from the first image, the first quantity of human effects is selected from the group consisting of:

computing devices;

notebooks;

beverage containers;

articles of clothing; and

bags.

18. The method of claim 15 , further comprising:

accessing a third image recorded by the sensor block at a third time;

detecting a third distribution of objects in the third image;

extracting a third set of features from the third image;

based on the third set of features:

detecting the work surface present in the work area; and

detecting a second quantity of humans within the distance threshold of the work surface;

updating the object entropy in the work area based on a second difference between the first distribution of objects, the second distribution of objects, and the third distribution of objects; and

in response to the second quantity of humans exceeding the human quantity threshold and in response to the object entropy falling below the entropy threshold, updating the occupancy status of the work surface to indicate the work surface as occupied with humans present.

19. A method for monitoring occupancy in a work area comprising:

at a first time:

accessing a first image recorded by a sensor block facing the work area;

detecting a work surface present in the work area in the first image;

detecting a first quantity of humans within a distance threshold of the work surface in the first image; and

in response to the quantity of humans exceeding a human quantity threshold, updating an occupancy status of the work surface to indicate the work surface as occupied with humans present; and

at a second time:

accessing a second image recorded by the sensor block;

detecting the work surface present in the work area in the second image;

detecting a second quantity of humans within the distance threshold of the work surface in the second image;

detecting a first cluster of objects within the distance threshold of the work surface in the second image; and

in response to the second quantity of humans falling below the human quantity threshold and in response to detecting the first cluster of objects within the distance threshold of the work surface, updating the occupancy status of the work surface to indicate the work surface as occupied with humans absent.

20. The method of claim 19 , further comprising:

at the first time, detecting a first distribution of objects in the first image;

at the second time, detecting a second distribution of objects in the second image; and

at a third time:

accessing a third image recorded by the sensor block;

detecting a third distribution of objects in the third image;

detecting the work surface present in the work area in the third image;

detecting absence of humans and absence of clusters of objects within the threshold distance of the work surface in the third image;

calculating an object entropy of the work area based on a difference between the first distribution of objects, the second distribution of objects, and the third distribution of objects; and

in response to detecting absence of humans and absence of clusters of objects within the threshold distance of the work surface and in response to the object entropy exceeding an entropy threshold:

updating the occupancy status of the work surface to indicate the work surface as vacant; and

generating a notification to alert maintenance staff to straighten the work area.