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Raspberry Pi based endangered species monitoring

The project aims at developing a classifier running on Raspberry pi for a low cost solution for monitoring endangered species such as tigers

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The project aims at developing a classifier running on Raspberry pi for a low cost solution for monitoring endangered species such as tigers. The model will be a variant of the squeeze net which takes 4.8 MB hardware space. The problem at hand does not require a lot of processing power and considering the size and time efficiency of models like squeezenet I am trying to figure out how to bring the effectiveness and accuracy of neural networks to low cost solution such as raspberry pi. Monitoring the endangered species is one cause that I am particularly interested in as these animals are a vert important part of the foodchain.

The project aims at developing a detector running on Raspberry pi as a low cost solution for monitoring endangered species such as tigers. The model will be a variant of the squeeze net which takes 4.8 MB hardware space. Squeezenet is a derivative of ALexnet and is used for classification. I am trying to implement image detection using the classification head of the squeezenet and adding a bounding box regressor that regresses over the grid patches in which the image would be divided to find the animal and locate it. The purpose of detection is to detect each instance of the tiger and to differentiate them. One of the biggest problems in tiger population counting is to differentiate between the individual animals. This is currently done on the basis of tigers paw marks and other differentiable properties. However with the use of computer vision and deep learning I feel we can make much better predictions without too much human intervention which is necessary to let these animals reside peacefully in the small space that they have in the woods. The problem at hand does not require a lot of processing power and considering the size and time efficiency of models like squeezenet I am trying to figure out how to bring the effectiveness and accuracy of neural networks to low cost solution such as raspberry pi. Monitoring the endangered species is one cause that I am particularly interested in as these animals are a vert important part of the foodchain.

I would be using tensorflow for the implementation. Some of the links that I am finding useful are:

https://github.com/samjabrahams/tensorflow-on-raspberry-pi

https://svds.com/tensorflow-image-recognition-raspberry-pi/

  • 1 × Raspberry Pi 3 - Model B - ARMv8 with 1G RAM The source of the pictures and the brains of the product
  • 1 × Raspberry Pi Camera Board v2 - 8 Megapixels Camera to take pictures
  • 1 × Raspberry Pi Sense HAT - For the Pi 3 / 2 / B+ / A+ To show the count
  • 1 × Raspberry Pi NoIR Camera Board - Infrared-sensitive Camera Camera to take pictures when its night

  • Initial Steps

    int-smart05/02/2017 at 21:57 0 comments

    Currently working on getting the logistics done and trying to figure out how to get a copy of squeezenet run on Raspberry Pi

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Paul Trebilcox-Ruiz wrote 05/03/2017 at 04:39 point

You'll have to let me know how that other OS goes for you. I've been doing something very similar on Android Things, and tossing together my instructions now https://hackaday.io/project/21692-wildlife-detector

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