Let's say I have designed an ML model that can take video input of a dog running around and give the breed of the dog as output. However, I do not want to wait for the video to finish before it is input into my model. I want something like the following to happen:

I am casually taking a video of my backyard when mid-way through a dog runs past the camera. Immediately, my model should identify (a) a dog has appeared within view and (b) the dog is a Labrador Retriever.

In an attempt to achieve the above, I have the following questions:

  1. Do I need to train a new model that detects when a dog has appeared within view?
  2. How can I make my model such that the input is continuous and that the model keeps running providing instant output?

Note 1: My ML model has been trained on Google Colaboratory using Keras. As of now, I have not established any connection between the camera and my model (I am unaware of how to do so). If any further details are required, I would be more than happy to share them!

Note 2: This question was originally posted on Stack Overflow. It was closed as a consequence of it being unfitting to the site. Link to the original post here.

Note 3: I also posted this question on Artificial Intelligence StackExchange. I was directed here. Link to the post on Artificial Intelligence StackExchange here.

  • $\begingroup$ Could you clarify how you currently get your video from your camera to any computing device? Do you already have a way to capture live feeds - if so, what is it, e.g. OpenCV, OBS or something bespoke that comes with the camera? $\endgroup$ Jul 20 at 8:00
  • $\begingroup$ @NeilSlater To be completely honest, this is a hypothetical question that I was thinking about. I do intend, however, to create something like the above as an interesting experiment. For now, I think you can assume that I use OpenCV to capture video feed from my CCTV camera (using something like this: capture = cv2.VideoCapture('rtsp://username:password@')) I am, however, completely open to any other suggestions for what to use. $\endgroup$ Jul 20 at 10:37

You do not need train a new model. You can find an existing neural network architecture that has been trained for "dog"/"not dog".

One option for continuous video input is use Python's OpenCV package which has a VideoCapture class which can read from an input stream. Individual frames can be extracted the stream. The model would predict "dog"/"not dog" on the frames.

Something like this:

import cv2

video = cv2.VideoCapture(0)

while True:
    _, frame = video.read()

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