A Gaussian Mixture Model Feature for Wildlife Detection

This paper addresses the challenge of the camouflage in the wildlife detection. The protective coloring makes the color space distance between the features of animal pattern and background pattern very small. The texture information should be considered under this situation. A reliable differential estimator for digital image data is employed. The estimation of the first order and the second order differentials of animal and background patterns are modelled using Gaussian mixture model method. It is shown the animal and background have bigger distance in Gaussian mixture models than in the color space. The mathematical expectation and standard deviation of the Gaussian models are therefore used to build the features to represent animal and background patterns. To demonstrate the performance of the proposed features, a neural network classifier is employed. The experiment results on wildlife scene images show that the proposed features have high classifying capacity to detect animals with camouflage from the background environment.