Abstract
Animal tracker is an important phase in animal behavior analysis. It leads to understanding how, when, and why the animal use the environmental resources, how, where, and when they interact with each other, with other species, and with their environment. Understanding the animal behavior is providing the link to population distribution which is essential for predicting the human-caused environmental change and guidance for conservation strategies. Tracking and detecting the animal is time and cost consuming. Machine Learning can relieve this burden by detecting animal automatically. Complex-Valued Neural Network is a method of Machine Learning that is challenging and interesting to be explored. This study applied of Complex-Valued Neural Network (CVNN) for animal tracking, especially in detecting the animal species. The experiment results present that CVNN is robust to recognition the animal automatically.
Highlights
The understanding of ecological systems based on very limited spatial and temporal scales is inadequate, so the ecologist needs to increase the scale of spatial dan temporal data
Animal tracker especially classification the animal is a field of ecology study that is able to be handled by machine learning
Wavelet Transform (WT) feature extraction is using time domain, the image in spatial domain has to transform in the time domain
Summary
The understanding of ecological systems based on very limited spatial and temporal scales is inadequate, so the ecologist needs to increase the scale of spatial dan temporal data. Solving this problem some scientist used technologies for automation spatial-temporal ecology study. Several researchers conducted research related to how to visualize, and analyze the ecology data automatically [2], tracking the animal using satellite imaging and detecting the animal automatically [3], analysis bird migration phenomena using machine learning [4], counting the population of zebrafish automatically using deep learning [5]. Schwager et al using GPS to analyzing the cow biological period using machine learning form the spatialtemporal data [6]
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