Bird-based high-frame-frequency sequence image abundance statistics and population identification algorithm

Abstract

The invention provides a high-frame-frequency sequence image bird abundance statistical algorithm based on a genetic algorithm KSW double-threshold segmentation algorithm fused with a distance transformation algorithm and a comprehensive algorithm of a high-frame-frequency sequence image bird population recognition algorithm based on a bird typical static feature data extraction fused machine learning algorithm. According to the method, the advantages of multiple algorithms are combined, a high-frame-frequency sequence image is used as a research object, the motion trail of a flying bird is predicted through the position change of two adjacent frames of motion targets, an effective research target is extracted, and the skeleton of the target is extracted through distance transformation operation, the adhesion shielding area in the target is separated through morphological processing, the abundance of the high-density bird flock is accurately counted, the problem that the abundance of the target with changeable postures and serious adhesion is difficult to count through an existing method is effectively solved, and the abundance counting accuracy is further improved.

Description

technical field [0001] The method relates to an image processing method, in particular to an image abundance statistics and population identification algorithm based on a high frame frequency sequence of birds, and belongs to the field of image processing. Background technique [0002] The ecological environment has gradually become one of the important indicators to consider the performance of the government. How to realize the harmonious coexistence with nature has become an urgent problem to be solved by the society. Bird abundance statistics and population identification are of great significance to biology, environmental protection and sustainable development of the country , is an important reference for ecological environment assessment; birds, as a social animal, often have inaccurate counts or even uncountable situations with the naked eye due to human visual errors in the process of static high-density abundance statistics. If the counting method Failure to improve...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the shortcomings of the existing method that it is difficult to count the abundance of static high-density birds and cannot automatically identify the species of flying birds, a high frame rate sequence image bird with KSW double-threshold segmentation algorithm based on genetic algorithm and distance transformation algorithm is provided. The comprehensive algorithm of species abundance statistics algorithm and bird population identification algorithm based on the extraction of typical static feature data of birds and the fusion machine learning algorithm of high frame frequency sequence images. This method combines the advantages of various algorithms and uses high frame frequency sequence images as The research object predicts the movement trajectory of the flying bird through the position change of the moving target in two adjacent frames, extracts the effective research target, extracts the skeleton of the target by using the distance transformation operation, and separates the sticky occlusion area existing in the target through morphological processing , and then accurately counting the abundance of high-density bird flocks can effectively solve the problem that existing methods are difficult to count the abundance of targets with variable postures and serious adhesion. It is difficult to separate even overlapping areas, further improving the accuracy of abundance statistics; using the high frame frequency sequence image bird population recognition algorithm based on the extraction of typical static feature data of birds combined with machine learning, not only can the bird image information data It can solve the problem that existing methods cannot automatically identify flying bird species while compressing the amount of information

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