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Your application is quite specific, I would first code a prototype using Matlab and Python, in order to define an algorithm, and only after that, I would develop the algorithm to work with the Raspberry Pi.

As first draft, I would propose the following steps:

• Database population (using pictures to which you manually associate name of the bird and so on). It could also integrate specific parts of the birds.
• Image alignment (the camera is static, but the bird could be in different position)
• Features definition (shape, colors, tail, beak, etc...)
• Feature collection (one vector for each features for example)
• Weight association (some features are more significant than others)
• Definition of a function that estimates the similarity between the features just extracted and the ones in the DB (example: using cosine similarity function)
• Classifier function (which decides the type of bird based on the distances calculated in the previous step)

Your application is quite specific, I would first code a prototype using Matlab and or Python, in order to define an algorithm, and only after that, I would develop the algorithm to work with the Raspberry Pi.

As first draft, I would propose the following steps:

• Database population (using pictures to which you manually associate name of the bird and so on). It could also integrate specific parts of the birds.
• Image alignment (the camera is static, but the bird could be in different position)
• Features definition (shape, colors, tail, beak, etc...)
• Feature collection (one vector for each features for example)
• Weight association (some features are more significant than others)
• Definition of a function that estimates the similarity between the features just extracted and the ones in the DB (example: using cosine similarity function)
• Classifier function (which decides the type of bird based on the distances calculated in the previous step)

Your application is quite specific, I would first code a prototype using Matlab or Python, in order to define an algorithm, and only after that, I would develop the algorithm to work with the Raspberry Pi.

As first draft, I would propose the following steps:

• Database population (using pictures to which you manually associate name of the bird and so on). It could also integrate specific parts of the birds.
• Image alignment (the camera is static, but the bird could be in different position)
• Features definition (shape, colors, tail, beak, etc...)
• Feature collection (one vector for each type of features for example)
• Weight association (some features are more significant than others)
• Definition of a function that estimates the similarity between the features just extracted and the ones in the DB (example: using cosine similarity function)
• Classifier function (which decides the type of bird based on the distances calculated in the previous step)