Hello all. As the title suggests, I am interested in extracting features from an egomotion image (an image taken from a car’s front dashcam). At the moment, I am using Resnet-50 but I don’t know whether this is the best one for this purpose. Honestly, I don’t understand how you would choose the appropriate neural network for this purpose.
Some more context: I am building a machine learning model for driving-related purposes. I want to predict the trajectory of objects that my car sees. For now, my plan is to extract features from the image at every time step and input those to an RNN encoder. With that in mind, what neural network is most likely to extract appropriate features. After extracting the features, I push them through a single fully-connected layer before going to the RNN encoder stage.
there are public egomotion datasets and trained models (FlowNet etc.), such as:
https://lmb.informatik.uni-freiburg.de/resources/datasets/StereoEgomotion/
you can start with them.
You might also consider to extract features using "classical approaches" rather than jumping directly to deep learning. If your interest is start experimenting with deep learning FlowNet seems an adequate starting point. Model Zoo is a repository to centralize open source deep learning code and pretrained models and has several pretrained models (among them the FlowNet).
Given that you are using images, maybe you could perform convolutional neural network analysis?
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