Systems and methods for automated object recognition
A method for recognizing an object in a video stream may include receiving a video stream from a video source, the video stream comprising a plurality of video frames. The method may also include selecting at least one video frame from the video frames according to a frame selection rate. The method may also include partitioning the selected video frame into a first plurality of image blocks. The method may also include recognizing, out of the first plurality of image blocks, a second plurality of image blocks which comprise an image of an object, the recognition being based on an image recognition parameter determined by a machine-learning algorithm. The method may also include determining that at least one of the second plurality of image blocks corresponds to the object based on a likelihood metric, the likelihood metric being determined by the processor based on at least the frame selection rate. The method may further include displaying, on a display, information identifying the object. A system and non-transitory computer-readable medium may also be provided.
1. A system for recognizing an object in a video stream, comprising:
a memory storing instructions; and
a processor configured to execute the stored instructions to:
receive the video stream from a video source, the video stream comprising a first set of video frames;
select at least one video frame from the first set of the video frames according to a frame selection rate, wherein the frame selection rate determines a number of the selected video frames;
partition the one or more selected video frames into one or more sets of image blocks, each set of image blocks corresponding to a respective video frame;
identify, within one or more sets of image blocks, a region which comprise an image of an object, the identification being based on a machine-learning algorithm for determining regions characterized by an image recognition parameter;
calculate a likelihood metric that the region corresponds to the object;
adjust the frame selection rate when the likelihood metric is less than a predetermined threshold; and
display, on a display, information identifying the object.
2. The system of claim 1 , wherein the processor is further configured to execute the stored instructions to apply the machine-learning algorithm to a second set of video frames.
3. The system of claim 1 , wherein the processor is further configured to execute the stored instructions to adjust the image recognition parameter in response to an input received from a user device.
4. The system of claim 1 , wherein determining that the region corresponds to the object comprises:
comparing the likelihood metric to a predetermined threshold; and
when it is determined that the likelihood metric exceeds or equals a predetermined threshold, determining that the region corresponds to the object.
5. The system of claim 1 , wherein the processor is further configured to execute the stored instructions to adjust the frame selection rate in response to an input from a user device.
6. The system of claim 1 , wherein the processor is further configured to execute the stored instructions to determine the frame selection rate based on at least one of an image quality of the video stream, a location of the object in the one or more selected video frames, or a viewable angle of the object in the one or more selected video frames.
7. The system of claim 1 , wherein the processor is further configured to execute the stored instructions to determine the likelihood metric based additionally on the information identifying the object.
8. The system of claim 7 , wherein the information identifying the object comprises information specifying at least one of a price of the object, availability of the object, or a location of the object.
9. The system of claim 1 , wherein the processor is further configured to execute the stored instructions to receive the information identifying the object from at least one data server.
10. The system of claim 1 , wherein:
the processor is further configured to execute the stored instructions to display the video stream; and
displaying the information identifying the object comprises displaying a digital shopping cart over the displayed video stream.
11. A computer-implemented method for recognizing an object in a video stream, comprising:
receiving the video stream from a video source, the video stream comprising a first set of video frames;
selecting at least one video frame from the first set of the video frames according to a frame selection rate, wherein the frame selection rate determines a number of the selected video frames;
partitioning the one or more selected video frames into one or more sets of image blocks, each set of image blocks corresponding to a respective video frame;
identifying within one or more sets, of image blocks, a region which comprise an image of an object, the identification being based on a machine-learning algorithm for determining regions characterized by an image recognition parameter;
calculating a likelihood metric that the region corresponds to the object;
adjusting the frame selection rate when the likelihood metric is less than a predetermined threshold; and
displaying, on a display, information identifying the object.
12. The method of claim 11 , further comprising:
applying the machine-learning algorithm to a second set of video frames.
13. The method of claim 11 , wherein determining that the region corresponds to the object comprises:
comparing the likelihood metric to a predetermined threshold; and
when it is determined that the likelihood metric exceeds or equals a predetermined threshold, determining that the region corresponds to the object.
14. The method of claim 11 , further comprising:
determining the frame selection rate based on at least one of an image quality of the video stream, a location of the object in the one or more selected video frames, or a viewable angle of the object in the one or more selected video frames.
15. The method of claim 11 , further comprising:
determining the likelihood metric based additionally on the information identifying the object.
16. A non-transitory computer-readable medium storing instructions which, when executed, cause at least one processor to perform operations for recognizing an object in a video stream, the operations comprising:
receiving the video stream from a video source, the video stream comprising a first set of video frames;
selecting at least one video frame from the first set of the video frames according to a frame selection rate, wherein the frame selection rate determines a number of the selected video frames;
partitioning the one or more selected video frames into one or more sets of image blocks, each set of image blocks corresponding to a respective video frame;
identifying within one or more sets, of image blocks, a region which comprise an image of an object, the identification being based on a machine-learning algorithm for determining regions characterized by an image recognition parameter;
calculating a likelihood metric that the region corresponds to the object;
adjusting the frame selection rate when the likelihood metric is less than a predetermined threshold; and
displaying, on a display, information identifying the object.
17. The non-transitory computer-readable medium of claim 16 , wherein the operations further comprise:
applying the machine-learning algorithm to a second set of video frames.
18. The non-transitory computer-readable medium of claim 16 , wherein determining that the region corresponds to the object comprises:
comparing the likelihood metric to a predetermined threshold; and
when it is determined that the likelihood metric exceeds or equals a predetermined threshold, determining that the region corresponds to the object.
19. The non-transitory computer-readable medium of claim 16 , wherein the operations further comprise:
determining the frame selection rate based on at least one of an image quality of the video stream, a location of the object in the one or more selected video frames, or a viewable angle of the object in the one or more selected video frames.
20. The non-transitory computer-readable medium of claim 16 , wherein the operations further comprise:
determining the likelihood metric based additionally on the information identifying the object.