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Title: Multi-modal people detection from aerial video
Author: Flynn, Helen
ISNI:       0000 0004 6061 3853
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2015
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There has been great interest in the use of small robotic helicopter vehicles over the last few years. Although there are regulatory issues involved in flying these that are still to be solved, they have the potential to provide a practical mobile aerial platform for a small fraction of the cost of a conventional manned helicopter. One potential class of applications for these is in searching for people, and this thesis explores a new generation of cameras which are suitable for this purpose. We propose HeatTrack, a novel algorithm to detect and track people in aerial imagery taken from a combined infrared/visible camera rig. A Local Binary Patterns (LBP) detector finds silhouettes in the infrared image which are used guide the search in the visible light image, and a Kalman filter combines information from both modalities in order to track a person more accurately than if only a single modality were available. We introduce a method for matching the thermal signature of a person to their corresponding patch in the visible modality, and show that this is more accurate than traditional homography-based matching. Furthermore, we propose a method for cancelling out camera motion which allows us to estimate a velocity for the person, and this helps in determining the location of a person in subsequent frames. HeatTrack demonstrates several advantages over tracking in the visible domain only, particularly in cases where the person shows up clearly in infrared. By narrowing down the search to the warmer parts of a scene, the detection of a person is faster than if the whole image were searched. The use of two imaging modalities instead of one makes the system more robust to occlusion; this, in combination with estimation of the velocity of a person, enables tracking even when information is lacking in either modality. To the best of our knowledge, this is the first published algorithm for tracking people in aerial imagery using a combined infrared/visible camera setup.
Supervisor: Cameron, Stephen Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Aerial videography ; Search and rescue operations ; Robotics