Computer vision systems and methods for automatically detecting, classifying, and pricing objects captured in images or videos
Systems and methods for automatically detecting, classifying, and processing objects captured in an images or videos are provided. In one embodiment, the system receives an image from an image source and detects one or more objects in the image. The system performs a high-level classification of the one or more objects in the image. The system performs a specific classification of the one or more objects, determines a price of the one or more objects, and generates a pricing report comprising a price of the one or more objects. In another embodiment, the system captures at least one image or video frame and classifies an object present in the image or video frame using a neural network. The system adds the classified object and an assigned object code to an inventory and processes the inventory to assign the classified object a price.
1. A system for automatically classifying and processing objects present in images or videos, comprising:
a memory;
and
a processor in communication with the memory, the processor:
capturing an image or a video frame;
classifying one or more objects present in the image or the video frame;
adding the classified objects to an inventory;
generating a set of fine-grained item codes related to the one or more classified objects, each of the fine-grained item codes indicative of different variations of possible residential item types corresponding to the one or more objects present in the image or the video frame; and
transmitting the inventory and at least one of the set of fine-grained item codes to a server in communication with the processor, the inventory and at least one of the set of fine-grained item codes processed at the server to generate a completed inventory with associated pricing information.
2. The system of claim 1 , wherein the processor:
extracts still image or video frames from a live camera feed,
resizes each of the still image or video frames based on a predetermined height and width, and
classifies one or more objects present in the resized still image or video frames.
3. The system of claim 2 , wherein the processor utilizes a tracking algorithm to track one or more objects moving through the live camera feed or appearing in and out of the live camera feed.
4. The system of claim 1 , further comprising a convolutional neural network.
5. The system of claim 1 , wherein the server is in communication with a pricing information database and the server determines a predetermined price of the classified object based on a user input and pricing information obtained from the pricing information database.
6. The system of claim 1 , wherein the server transmits the processed inventory to a third party system.
7. The system of claim 1 , wherein the processor utilizes a natural language processing algorithm to process audio data associated with the object present in the captured video frame.
8. A method for automatically classifying and processing an object present in an image or video comprising the steps of:
capturing an image or a video frame;
classifying one or more objects present in the images or the video frame;
adding the classified objects to an inventory;
generating a set of fine-grained item codes related to the one or more classified objects, each of the fine-grained item codes indicative of different variations of possible residential item types corresponding to the one or more objects present in the image or the video frame; and
transmitting the inventory and at least one of the set of fine-grained item codes to a server in communication with the processor, the inventory and at least one of the set of fine-grained item codes processed at the server to generate a completed inventory with associated pricing information.
9. The method of claim 8 , further comprising the steps of:
extracting still image or video frames from the live camera feed,
resizing each of the still image or video frames based on a predetermined height and width, and
classifying one or more objects present in the resized still image or video frames.
10. The method of claim 9 , further comprising the step of utilizing a tracking algorithm to track one or more objects moving through the live camera feed or appearing in and out of the live camera feed.
11. The method of claim 8 , wherein said classification step is performed using a convolutional neural network.
12. The method of claim 8 , wherein the server is in communication with a pricing information database and further comprising the step of modifying, by the server, the predetermined price of the classified object based on a user input and pricing information obtained from the pricing information database.
13. The method of claim 8 , further comprising the step of transmitting, by the server, the processed inventory to a third party system.
14. The method of claim 8 , further comprising the step of utilizing a natural language processing algorithm to process audio data associated with the object present in the captured video frame.
15. A non-transitory computer readable medium having instructions stored thereon for automatically classifying and processing an object present in an image or a video which, when executed by a processor, causes the processor to carry out the steps of:
capturing an image or a video frame;
classifying one or more objects present in the images or the video frame;
adding the classified objects to an inventory;
generating a set of fine-grained item codes related to the one or more classified objects, each of the fine-grained item codes indicative of different variations of possible residential item types corresponding to the one or more objects present in the image or the video frame; and
transmitting the inventory and at least one of the set of fine-grained item codes to a server in communication with the processor, the inventory and at least one of the set of fine-grained item codes processed at the server to generate a completed inventory with associated pricing information.
16. The non-transitory computer readable medium of claim 15 , the processor further carrying out the steps of:
extracts still image or video frames from the live camera feed,
resizes each of the still image or video frames based on a predetermined height and width, and
classifies one or more objects present in the resized still image or video frames.
17. The non-transitory computer readable medium of claim 16 , the processor further carrying out the step of utilizing a tracking algorithm to track one or more objects moving through the live camera feed or appearing in and out of the live camera feed.
18. The non-transitory computer readable medium of claim 15 , wherein the server is in communication with a pricing information database and the server modifies the predetermined price of the classified object based on a user input and pricing information obtained from the pricing information database.
19. The non-transitory computer readable medium of claim 15 , the processor further carrying out the step of utilizing a natural language processing algorithm to process audio data associated with the object present in the captured video frame.