Automatic detection of honeybees in a hive
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
The complex social structure of the honey bee hive has been the subject of inquiry since the dawn of science. Studying bee interaction patterns could not only advance sociology but find applications in epidemiology as well. Data on bee society remains scarce to this day as no study has managed to comprehensively catalogue all interactions among bees within a single hive. This work aims at developing methodologies for fully automatic tracking of bees and their interactions in infrared video footage.
H.264 video encoding was investigated as a means of reducing digital video storage requirements. It has been shown that two orders of magnitude compression ratios are attainable while preserving almost all information relevant to tracking.
The video images contained bees with custom tags mounted on their thoraxes walking on a hive frame. The hive cells have strong features that impede bee detection. Various means of background removal were studied, with the median overone hour found to be the most effective for both bee limb and tag detection. K-means clustering of local textures shows promise as an edge filtering stage for limb detection.
Several tag detection systems were tested: a Laplacian of Gaussian local maxima based system, the same improved with either support vector machines or multilayer perceptrons, and the Viola-Jones object detection framework. In particular, this work includes a comprehensive description of the Viola-Jones boosted cascade with a level of detail not currently found in literature. The Viola-Jones system proved to outperform all others in terms of accuracy. All systems have been found to run inreal-time on year 2013 consumer grade computing hardware. A two orders of magnitude file size reduction was not found to noticeably reduce the accuracy of any tested system.
Place, publisher, year, edition, pages
IT, 13 060
Engineering and Technology
IdentifiersURN: urn:nbn:se:uu:diva-206838OAI: oai:DiVA.org:uu-206838DiVA: diva2:645634
Master Programme in Computer Science
Brun, AndersChristoff, Ivan