The current context of biodiversity loss is particularly marked by the Colony Collapse Disorder (CCD) of honeybees due to multiple causes, toxicological, parasitic and viral. The beekeeper has to face these difficulties in order to maintain the population of bees to save the species but also to make its exploitation profitable. According studies, one can understand what is happening inside the hive by observing what is going on outside. In this context, we propose to individually capture by video the flight trajectories of bees and then characterize the pace of the global activity in front of the hive to infer observations that will be consolidated and made available to apicultural data scientists. Thus bee are detected and tracked using image and video processing methods, then the trajectory are modeled. Then, from the extracted data outcome of the videos, curves are fitted as the ideal trajectories of each bee path in order to study and classify their behaviors. Thus, for each tracked bee, the points of extracted centered positions are time-ordered approximated on a plan. The chosen method interpolates the abscissae separately from the ordinates as time-dependent functions before plotting the parametric curve for each bee path individually. Thus, the abscissae as the ordinates are interpolated using cubic splines. The consecutive points to be interpolated are connected by polynomials of degree three. The first and second derivatives of these polynomials must be connected too. This allows the curve to look more natural by avoiding tingling and convexity discontinuities. Finally, it represents the continuity of the speed of the bees too. Experiments on synthetic and real videos show precise detections of the bee paths. Looking forward, through the collected data, the bee behavior could be understood by using machine learning and the semi supervised method must be one way to proceed.
This paper is dedicated to a new anisotropic diffusion approach for image regularization based on a gradient and two diffusion directions obtained from half Gaussian kernels. This approach results in smoothing an image while preserving edges. From an anisotropic edge detector, built of half Gaussian derivative kernels, we introduce a new smoothing method preserving structures which drives the diffusion function of the angle between the two edge directions and the gradient value. Due to the two directions diffusion used in the control function, our diffusion scheme enables to preserve edges and corners, contrary to other anisotropic diffusion methods. Moreover, parameters of the Gaussian kernel can be tuned to be sufficiently thin extracting precisely edges whereas its length allows detecting in contour orientations which leads to a coherent image regularization. Finally, we present some experimental results and discuss about the choice of the different parameters.