METHOD AND SYSTEM FOR DETECTING INVENTORY ANOMALIES USING CAMERAS
Disclosed are systems and methods for monitoring inventory within a warehouse using mediate data streams. A server monitors and manages camera data streams for particular footage or segments of interest associated with exceptions of product inventory, such as misplaced or missing products. The server receives media data from cameras of a warehouse. The server may receive or detect an exception, which indicates the nature of the exception, the product, and other information. In response, the server performs certain actions, including selecting or identifying target cameras that produced footage of the product. The server extracts segments of the media from the selected cameras according to timestamps associated with the exception. The server then sends the segments to a client device for review, and stores the segments into a database.
1 . A computer-implemented method comprising:
obtaining, by a computer, an exception indicating a product identifier associated with a product of an order;
determining, by the computer, based upon the exception indicating the product identifier, a set of one or more cameras of a plurality of cameras based on product data associated with the product and camera data indicating the set of one or more cameras having corresponding fields of view including the product, at least one camera of the set of one or more cameras having a corresponding field of view including an area for a transfer of the product of the order using an autonomous vehicle;
for at least one camera of the set of one or more cameras:
identifying, by the computer in media data received from the plurality of cameras, at least one segment of the media data received from the at least one camera corresponding to a time period relevant to the exception; and
transmitting, by the computer to a client device, the identified at least one segment of the media data.
2 . The method according to claim 1 , wherein identifying the segment of the media data received from a particular camera in the set of one or more cameras includes:
parsing, by the computer, the media data received from the particular camera according to one or more timestamps of product data, thereby generating the segment of the media data for the particular camera.
3 . The method according to claim 1 , wherein determining the set of one or more cameras associated with the product of the order includes:
correlating, by the computer, camera data associated with the plurality of cameras with product data to identify the set of one or more cameras, the camera data of each particular camera in the set of one or more cameras with the product of the order including one or more data intersections with the product data.
4 . The method according to claim 1 , wherein obtaining the exception associated with the order includes:
identifying, by the computer, one or more differences between the segment of the media data received from a particular camera of the set of one or more cameras and expected media data stored in the non-transitory storage; and
detecting, by the computer, the exception for the product of the order responsive to the computer determining that the one or more differences satisfy a detection threshold.
5 . The method according to claim 1 , further comprising storing, by the computer, into non-transitory storage the segment of the media data from the at least one camera of the set of one or more cameras according to a retention policy.
6 . The method according to claim 1 , further comprising instructing, by the computer, the at least one camera of the set of one or more cameras to increase a media quality for the media data received from the at least one camera.
7 . The method according to claim 1 , further comprising identifying, by the computer, a particular segment of the media data from the set of one or more cameras having an object image of the product.
8 . The method according to claim 1 , wherein the exception includes product data indicating the product of the order, the product data including at least one of:
a product identifier,
a scan of the product identifier,
a product image,
a bin identifier associated with the product,
an autonomous vehicle identifier for the autonomous vehicle,
a worker identifier,
a worker image, or
one or more timestamps.
9 . A system comprising:
a computer comprising a processor configured to:
obtain an exception indicating a product identifier associated with a product of an order;
determine based upon the exception indicating the product identifier, a set of one or more cameras of a plurality of cameras based on product data associated with the product and camera data indicating the set of one or more cameras having corresponding fields of view including the product, at least one camera of the set of one or more cameras having a corresponding field of view including an area for a transfer of the product of the order using an autonomous vehicle;
for at least one camera of the set of one or more cameras:
identify, in media data received from the plurality of cameras, at least one segment of the media data received from the at least one camera corresponding to a time period relevant to the exception; and
transmit, to a client device, the identified at least one segment of the media data.
10 . The system according to claim 9 , wherein when identifying the segment of the media data received from the particular camera in the set of one or more cameras, the computer is further configured to:
parse the media data received from the particular camera according to one or more timestamps of product data, thereby generating the segment of the media data for the particular camera.
11 . The system according to claim 9 , wherein when determining the set of one or more cameras associated with the product of the order, the computer is configured to:
correlate camera data associated with the plurality of cameras with product data to identify the set of one or more cameras, the camera data of each particular camera in the set of one or more cameras with the product of the order including one or more data intersections with the product data.
12 . The system according to claim 9 , wherein when obtaining the exception associated with the order, the computer is further configured to:
identify one or more differences between the segment of the media data received from the particular camera and expected media data stored in the non-transitory storage; and
detect the exception for the product of the order responsive to determining that the one or more differences satisfy a detection threshold.
13 . The system according to claim 9 , wherein the computer is further configured to store, into non-transitory storage, the segment of the media data from the at least one camera of the set of one or more cameras according to a retention policy.
14 . The system according to claim 9 , wherein the computer is further configured to instruct the at least one camera of the set of one or more cameras to increase a media quality for the media data received from the at least one camera.
15 . The system according to claim 9 , wherein the computer is further configured to identify a particular segment of the media data from the set of one or more cameras having an object image of the product.
16 . The system according to claim 9 , wherein the exception includes product data indicating the product of the order, the product data including at least one of:
a product identifier,
a scan of the product identifier,
a product image,
a bin identifier associated with the product,
an autonomous vehicle identifier for the autonomous vehicle,
a worker identifier,
a worker image, or
one or more timestamps.
17 . A non-transitory machine-readable storage medium having computer-executable instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
obtaining an exception indicating a product identifier associated with an product of an order;
determining based upon the exception indicating the product identifier, a set of one or more cameras of a plurality of cameras based on product data associated with the product identifier and camera data indicating the set of one or more cameras having corresponding fields of view including the product, at least one camera of the set of one or more cameras having a corresponding field of view including an area for a transfer of the product of the order using an autonomous vehicle;
for at least one camera of the set of one or more cameras:
identifying, in media data received from the plurality of cameras, at least one segment of the media data received from the at least one camera corresponding to a time period relevant to the exception; and
transmitting, to a client device, the identified at least one segment of the media data.
18 . The non-transitory machine-readable storage medium of claim 17 , wherein the computer-executable instructions further cause the one or more processors to perform the operations comprising:
parsing the media data received from the particular camera according to one or more timestamps of product data, thereby generating the segment of the media data for the particular camera.
19 . The non-transitory machine-readable storage medium of claim 17 , wherein the computer-executable instructions further cause the one or more processors to perform the operations comprising:
correlating camera data associated with the plurality of cameras with product data to identify the set of one or more cameras, the camera data of each particular camera in the set of one or more cameras with the product of the order including one or more data intersections with the product data.
20 . The non-transitory machine-readable storage medium of claim 17 , wherein the computer-executable instructions further cause the one or more processors to perform the operations comprising:
identifying one or more differences between the segment of the media data received from the particular camera and expected media data stored in the non-transitory storage; and
detecting the exception for the product of the order responsive to that computer determining that the one or more differences satisfy a detection threshold.