Lets say I have a screenshot like this:
I want to be able to detect/localize each item on the floor, however, there 1) can be any number of items in the image and 2) each item is different
I have a candidate list of all possible items. In reality each one is labelled and separated into individual image files:
I've thought about training something like a convnet, but that feels like it might be slow because I'd need to segment each screenshot with multiple sliding windows and feed each one forward through the net. Creating those sliding window segments for each screenshot will likely take a long time. I'd like the entire detection process to be completed quickly (<1sec)
Whats the most efficient way of doing this detection task? I will be implementing this using Javascript
The main problems are:
- The item count is unknown. There could be 0 items in the screenshot, or there could be many
- There are a lot of possible targets, each one of which are different in shape, colour, and structure
- Javascript isn't the fastest of languages for this type of work. This is a semi strict requirement unless its absolutely impossible to do this using JS. The fallback language would be Python