Weighted clustering for bees detection on video images

Abstract

This work describes a bee detection system to monitor bee colony conditions. The detection process on video images has been divided into 3 stages: determining the regions of interest (ROI) for a given frame, scanning the frame in ROI areas using the DNN-CNN classifier, in order to obtain a confidence of bee occurrence in each window in any position and any scale, and form one detection window from a cloud of windows provided by a positive classification. The process has been performed by a method of weighted cluster analysis, which is the main contribution of this work. The paper also describes a process of building the detector, during which the main challenge was the selection of clustering parameters that gives the smallest generalization error. The results of the experiments show the advantage of the cluster analysis method over the greedy method and the advantage of the optimization of cluster analysis parameters over standard-heuristic parameter values, provided that a sufficiently long learning fragment of the movie is used to optimize the parameters.