Automatic detection of flying bird species using computer vision techniques

Abstract

Bird population is an important factor that may affect ecology of the an area. The main aim is to create a solution for counting different species of birds present in an area and classify them into categories. There are around 1300 species of birds found in India and there can be chance that a new species which remained unidentified till now. We can calculate the number of bird species available in a locality and keep a track whether any species are in risk of being endangered. Calculating the bird population can help the ecologist to search the problem which may endanger them. Manual labor for counting and searching for new species is time consuming and error prone. In the present work, The method of solution is to create a computer vision system using machine learning techniques or deep learning method for a better accurate results. Automatic Bird detection system is primarily useful in providing optimal bird count in region with counting, the system also classifies the bird based upon its species and features.after detecting the bird, the segregates the data according to the bird’s features. This data which is stored in the database can be accessed by scientists, photographers, and also by surveying units as this data provides important information about the number of birds in the area, the type of birds, Their features, and also this data helps scientists in predicting the environmental changes in the particular area by analysing the type of birds visiting the area based upon seasonal changes.

Automated Bird Counting with Deep Learning for Regional Bird Distribution Mapping

“…The methods applied in the discussed papers vary from traditional image analysis techniques including thresholding and template matching to segmentation and supervised/unsupervised classification, but still, no deep learning method is mentioned. Even though papers related to machine learning applications on unmanned aerial vehicle- (UAV) or satellite-derived images have been published so far [ 43 , 44 , 45 , 46 , 47 ], the studies focusing on on-ground photography are limited [ 48 , 49 ]. At the same time, digital photography has entered a new era with the availability of cloud-storage and location-aware cameras and smart-phones that enable the sharing of photographs on a truly massive scale.…”