Publication Date


Document Type


First Advisor

Butail, Sachit

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Mechanical Engineering


With their agile maneuvers and aerodynamic feats, flying insects continue to inspire the design, sensing, and control of unmanned aerial vehicles. Among the different behaviors that such insects perform, the mating pursuit stands out in terms of the high speeds and quick turns that each insect performs during the chase. Despite their frequent occurrence in nature, pursuit events between mating insects are rarely analyzed. This is because mating events are difficult to film in the wild or elicit in laboratory settings. The long-term goal of this research is to quantify the kinematics of flying insects in pursuit. The specific objective of this thesis is to apply methods from dynamic estimation and computer vision to develop an automated tracking algorithm for reconstructing three-dimensional flight of insects in the wild.

A full-state measurement model that uses epipolar geometry on image optical flow is incorporated into a Kalman filtering algorithm to estimate both three-dimensional position and velocity of a moving target. The tracking approach is validated on a dataset obtained in the laboratory by tracking a rigid body marker in a motion capture facility, where the full-state measurement model reduces the error in speed by more than 70% over the standard partial-state measurement model.

The full-state measurement Kalman filtering algorithm is incorporated into a user-assisted tracking system to reconstruct the pursuit kinematics of damselflies filmed in the wild. Image segmentation parameters are optimized on the basis of Hausdorff distance to best isolate individual insects as they fly against a cluttered and moving background. To permit the use of hand-held devices, camera motion is removed automatically by tracking still objects identified using background optical flow. The approach developed in this thesis has the potential to vastly increase the amount of insect pursuit kinematic data that can be used to inspire the sensing and control strategies of micro unmanned aerial vehicles.


64 pages




Northern Illinois University

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