Recognition of an animal in pictures

Question

I am facing a challenging problem. On the courtyard of company I am working is a camera trap which takes a photo of every movement. On some of these pictures there are different kinds of animals (mostly deep gray mice) that cause damages to our cable system. My idea is to use some application that could recognize if there is a gray mouse on the picture or not. Ideally in realtime. So far we have developed a solution that sends alarms for every movement but most of alarms are false. Could you provide me some info about possible ways how to solve the problem?

Answer 1

In technical parlance, what you describe above is often called event detection. I know of no ready-made approach to solve all of this at once, but with a little bit of programming you should be all set even if you don't want to code any computer vision algorithms or some such.

The high-level pipeline would be:

• Making sure that your video is of sufficient quality. Gray mice sound kind of tough, plus the pictures are probably taken at night - so you should have sufficient infrared lighting etc. But if a human can make it out whether an alarm is false or true, you should be fine.

• Deploying motion detection and taking snapshot images at the time of movements. It seems like you have this part already worked out, great! Detailing your setup could benefit others. You may also need to crop only the area in motion from the image, are you doing that?

• Building an archive of images, including your decision of whether they are false or true alarm (labels in machine learning parlance). Try to gather at least a few tens of example images for both cases, and make them representative of real-world variations (do you have the problem during daytime as well? is there snowfall in your region?).

• Classifying the images taken from the video stream snapshot to check whether it's a false alarm or contains bad critters eating cables. This sounds tough, but deep learning and machine learning is making advances by leaps; you can either:

  • deploy your own neural network built in a framework like caffe or Tensorflow (but you will likely need a lot of examples, at least tens of thousands I'd say)
  • use an image classification API that recognizes general objects, like Clarifai or Imagga - if you are lucky, it will notice that the snapshots show a mouse or a squirrel (do squirrels chew on cables?), but it is likely that on a specialized task like this one, these engines will get pretty confused!
  • use a custom image classification API service which is typically even more powerful than rolling your own neural network since it can use a lot of tricks to sort out these images even if you give it just a small number of examples for each image category (false / true alarm here); vize.it is a perfect example of that (anyone can contribute more such services?).

The real-time aspect is a bit open-ended, as the neural networks take some time to process an image — you also need to include data transfer etc. when using a public API, but if you roll out your own, you will need to spend a lot of effort to get low latency as the frameworks are by default optimized for throughput (batch prediction). Generally, if you are happy with ~1s latency and have a good internet uplink, you should be fine with any service.

Disclaimer: I'm one of the co-creators of vize.it.

Answer 2

How about getting a cat?

Also, you could train your own custom classifier using the IBM Watson Visual Recognition service. (demo: https://visual-recognition-demo.mybluemix.net/train ) It's free to try and you just need to supply example images for the different categories you want to identify. Overall, Petr's answer is excellent.