What are some solutions for dealing with time series data that are recorded at uneven intervals?
So, the question essentially asks how to model sequential data with inconsistent time intervals.
I would say option 2 would be more logical in my mind.
I would propose to have the timestamp when the image was taken from the camera as an input feature for each example. Here, it would still be in a format which you could model this using an RNN/LSTM (any sequential model architecture).
By adding the timestamp feature to your input, you at least have given the model the time representation of the images. You could represent the timestamp fo the image as number of seconds (minutes/hours, whichever is more appropriate to the dataset you have) from the earliest image you have in the dataset.
Of course, it's best to normalise this so you don't end up with very large values, which could reduce the overall generalisation performance of the model.