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Sorting the types of birds using computer vision

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Bird populations are identified as important biodiversity indicators, so collecting reliable population data is important to ecologists and scientists. However, existing manual monitoring methods are labour-intensive, time-consuming, and potentially error prone. The aim of our work is to develop a reliable automated system, capable of classifying the species of individual birds, during flight, using video data. This is challenging, but appropriate for use in the field, since there is often a requirement to identify in flight, rather than while stationary. We present our work, which uses a new and rich set of appearance features for classification from video. We also introduce motion features including curvature and wing beat frequency. Combined with Normal Bayes classifier and a Support Vector Machine classifier, we present experimental evaluations of our appearance and motion features across a data set comprising 7 species. Using our appearance feature set alone we achieved a classification rate of 92% and 89% (using Normal Bayes and SVM classifiers respectively) which significantly outperforms a recent comparable state-of-the-art system. Using motion features alone we achieved a lower-classification rate, but motivate our on-going work which we seeks to combine these appearance and motion feature to achieve even more robust classification.



BIRD behavior and population trends have become an important issue now a days. Birds help us to detect other organisms in the environment (e.g. insects they feed on) easily as they respond quickly to the environmental changes [2]. But, gathering and collecting information about birds requires huge human effort as well as becomes a very costlier method. In such case, a reliable system that will provide large scale processing of information about birds and will serve as a valuable tool for researchers, governmental agencies, etc. is required. So, bird species identification plays an important role in identifying that a particular image of bird belongs to which species. Bird species identification means predicting the bird species belongs to which category by using an image.

The identification can be done through image, audio or video. An audio processing technique makes it possible to identify by capturing the audio signal of birds. But, due to the mixed sounds in environment such as insects, objects from real world, etc. processing of such information becomes more complicated. Usually, human beings find images more effective than audios or videos. So, an approach to classify bird using an image over audio [8] or video is preferred. Bird species identification is a challenging task to humans as well as to computational.


Existing System:

A number of existing attempts to automate the identification of birds have used audio rather than visual signals, such as (Briggs et al., 2009; Neal et al., 2011; Lopes et al., 2011; Bardeli et al., 2010). The use of audio signals has some attractive features; species typically have distinctive calls, and no line of sight is necessary to detect audio. However, there is also significant disadvantages. Audio signals are sparse (an individual may emit no audio at all for extended periods), and it is not realistic to differentiate individuals in this way (e.g. for counting). For this reason, a small but growing number of studies have looked at computer vision.


Proposed System:

the proposed system. To develop such system a trained dataset is required to classify an image. Trained dataset consists of two parts trained result and test result. The dataset has to be retrained to achieve higher accuracy in identification using in Google Collab. The training dataset is made using 50000 steps taking into consideration that higher the number of steps higher is its accuracy. The accuracy of training dataset is 93%. The testing dataset consists of nearly 1000 images with an accuracy of 80%.



  • High accuracy
  • Low complex
  • Recognition of audios been classified without any noise



  • Accuracy is less
  • Illumination occurrences is less



  • Real Time applications


Software Requirement:

  • Python Idle
  • Opencv
  • Numpy
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  • Instructor pantech team
  • Duration 15 Hrs
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  • Access 3 Months

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