Cascade classifier for the detection and identification of birds in a videostream

A method and a prototype of the program for detecting the presence of birds in the video data flow in real time are presented in the paper. The method is based on the cascade classifier solving the problem of bird detection and identification with the use of a bioacoustic bird scaring system deployed at the Tomsk airport. In our research, the Viola-Jones cascade classifier representing one of the implementations of the Haar cascade algorithm has been used. This algorithm allows objects to be detected in images and videos with high accuracy and rate. In this case, the classifier was leaned on the data set containing images of birds that allowed us to reach high accuracy of bird detection and identification in the videos. The possibilities of the developed classifier are also estimated, and its high productivity is shown. In this study, various methods of machine learning and video data analysis are used to obtain exact and reliable results. As a whole, the present work is an innovative approach to a solution to the urgent problem of airport protection from birds. The application of the developed method has allowed the operating efficiency of the bioacoustic bird scaring system to be increased together with the safety of flights at the Tomsk airport, thereby decreasing the probability of airplane collisions with birds. The novelty of the work consists of the application of the Viola–Jones method for solving the problem of bird detection and identification and estimating its efficiency. Thus, this work is an important contribution to the development of methods for detecting and identifying objects in videos and can also be used in other fields of science and technology in which the automatic detection and classification of objects in the video data flow is required.