Background extraction method for analysis of natural images captured by camera traps

Introduction: Automatic detection of animals, particularly birds, on images captured in the wild by camera traps remains an unsolved task due to the shooting and weather conditions. Such observations generate thousands or millions of images which are impossible to analyze manually. Wildlife sanctuaries and national parks normally use cheap camera traps. Their low quality images require careful multifold processing prior to the recognition of animal species. Purpose: Developing a background extraction method based on Gaussian mixture model in order to locate an object of interest under any time/season/meteorological conditions. Results: We propose a background extraction method based on a modified Gaussian mixture model. The modification uses truncated pixel values (in low bites) to decrease the dependence on the illumination changes or shadows. After that, binary masks are created and processed instead of real intensity values. The proposed method is aimed for background estimation of natural scenes in wildlife sanctuaries and national parks. Structural elements (trunks of growing and/or fallen trees) are considered slowly changeable during the seasons, while other textured areas are simulated by texture patterns corresponding to the current season. Such an approach provides a compact background model of a scene. Also, we consider the influence of the time/season/meteorological attributes o f a scene with respect to its restoration ability. The method was tested using a rich dataset of natural images obtained on the territory of Ergaki wildlife sanctuary in Krasnoyarsk Krai, Russia. Practical relevance: The application of the modified Gaussian mixture model provides an accuracy of object detection as high as 79-83% in the daytime and 60-69% at night, under acceptable meteorological conditions. When the meteorological conditions are bad, the accuracy is 5-8% lower.