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I am given a selection of videos of users exploring simulated 3D enviroments (kind of looks like the Sims video game) and I am tasked with being able to classify each room using a tensorflow framework. E.g. if a user were to hover over a certain area in the environment, the model should be able to classify whether it's a security line

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or baggage check etc.

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I am fairly new to machine learning algorithms and techniques, however I have been given a working VGG16 CNN model that is able to characterize (given labeled input data) actors in a video clip, and I am expected to rework this into environment classification. Is such a thing possible given this framework? I am undecided on whether I should collect images from various angles of the room/environment, and labeling and training each image as a whole using VGG16/Resnet50, or to implement some sort of R-CNN/YOLO model using bounding boxes to classify the environment given known objects found on screen. I am not actually limited to the given tensorflow framework, so if there is a better way to approach this problem I can recreate a new code environment from scratch if needed.

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It depends on the objective you have: if the objective is just to recognize the environment, choose a simple image recognition model first. Furthermore, simulated environment are cleaner than real ones (no shadows, no noise). Consequently, there are good chances to reach very good results with a fast model (like Inception) that would also have a very good processing time.

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