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A cascaded method to detect aircraft in video imagery

Debadeepta Dey, Christopher M. Geyer, Sanjiv Singh and and Matthew Digioia
Journal Article, Carnegie Mellon University, The International Journal of Robotics Research, Vol. 30, No. 12, pp. 1527 - 1540, October, 2011

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Unmanned Aerial Vehicles (UAVs) have played vital roles recently in both military and non-military applications. One of the reasons UAVs today are unable to routinely fly in US National Airspace (NAS) is because they lack the sense and ability to avoid other aircraft. Although certificates of authorization can be obtained for short-term use, it entails significant delays and bureaucratic hurdles. Therefore, there is a great need to develop a sensing system that is equivalent to or has greater performance than a human pilot operating under Visual Flight Rules (VFR). This is challenging because of the need to detect aircraft out to at least 3 statute miles, while doing so on field-of-regard as large as 30◦ ( vertical) ×220◦ ( horizontal) and within the payload constraints of a medium-sized UAV In this paper we report on recent progress towards the development of a field deployable sense-and-avoid system and concentrate on the detection and tracking aspect of the system. We tested a number of approaches and chose a cascaded approach that resulted in 100% detection rate (over about 40 approaches) and 98% tracking rate out to 5 statute miles and a false positive rate of 1 every 50 frames. Within a range of 3.75 miles we can achieve nearly 100% tracking rate.

BibTeX Reference
title = {A cascaded method to detect aircraft in video imagery},
author = {Debadeepta Dey and Christopher M. Geyer and Sanjiv Singh and and Matthew Digioia},
booktitle = {The International Journal of Robotics Research},
keyword = {Sense-and-avoid, UAV field robotics, perception,},
school = {Robotics Institute , Carnegie Mellon University},
month = {October},
year = {2011},
volume = {30},
number = {12},
pages = {1527 - 1540},
address = {Pittsburgh, PA},