Dear community,
I would like to share a POC video of my cool inventory tracking project. In this project, a video stream and detections are fed to an object tracking algorithm to count the number of each inventory item that appeared in the video.
I have first labeled inventories in each frame of the video. Trained object detection model on labeled data, and used roboflow trackers library to associate detections in each frame.
That seems really cool. My question is how this can be expanded from a proof of concept into actual usage. How would you expand this models detection to include potentially hundreds or thousands of products as found in most warehouses and retail stores in a different environment to what is tested? And how are the counts being stored and is it possible to pass these counts to a central store server?
Thanks for your comment. To obtain more production grade accuracy one can adopt text detection model, and perform OCR on detected texts, than count them over the video. It is a tough problem anyway.
I have first labeled inventories in each frame of the video. Trained object detection model on labeled data, and used roboflow trackers library to associate detections in each frame
Everything seems stable and nothing seems to be counted twice. Does roboflow trackers handle counting natively?
Furthermore, is this built to spec? If not, it might be good to use a variety of items. Or some that have been mangled slightly. Doing such things in the context of external train data used on unseen, untrained test footage may add value from the perspective of stakeholders. Thats just an unsolicited suggestion. Take it as you wish. In any case, great project. What are your plans for it?
