Using machine learning and Maya to identify free-range wild giant panda

Yi Lin

doi:10.1117/12.2631275

18 March 2022 Using machine learning and Maya to identify free-range wild giant panda

Yi Lin

Proceedings Volume 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021); 121680D (2022) https://doi.org/10.1117/12.2631275
Event: International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 2021, Harbin, China

Abstract

Machine learning can be applied to obtain detailed information about wild giant pandas in a specific area, such as number, age, and gender; and using Maya to create a similar environment and adjust the pressure and sunshine. According to the previous work and experience, the aforementioned statistical work on the number of wild giant pandas in China is quite a time-consuming task up to three to four years. However, it is too late to formulate protection plans and strategies from the observation and exploration of pandas. The current development of machine learning will allow scientists and researchers to attain valuable and reliable information automatically analyzed, and even there is no need for researchers living in the forest. Here, we mainly demonstrate the application of the footprint identification technique which the computer divides the picture into two groups with footprints or not and then identifies which footprints belong to pandas from all animals. Furthermore, it will be capable of classifying and clustering their number, sex and age. It is based on the algorithm of k-nearest neighbor and clustering collected panda images. Moreover, it is also can be applied to another animal with a free-ranging living habitat seeking conservation without the invasion of a human being.

Citation Download Citation

Yi Lin "Using machine learning and Maya to identify free-range wild giant panda", Proc. SPIE 12168, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021), 121680D (18 March 2022); https://doi.org/10.1117/12.2631275

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