An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Noniterative Greedy Algorithm for Multiframe Point Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Learning to detect aircraft at low resolutions
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
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Unmanned Aerial Vehicles (UAVs) have played vital roles recently in both military and non-military applications. Oneof the reasons UAVs today are unable to routinely fly in US National Airspace (NAS) is because they lack the senseand ability to avoid other aircraft. Although certificates of authorization can be obtained for short-term use, it entailssignificant 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 challengingbecause of the need to detect aircraft out to at least 3 statute miles, while doing so on field-of-regard as large as30脗掳( vertical) 脙聴 220脗掳( 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 detectionand tracking aspect of the system. We tested a number of approaches and chose a cascaded approach that resulted in100% detection rate (over about 40 approaches) and 98% tracking rate out to 5 statute miles and a false positive rate of 1every 50 frames. Within a range of 3.75 miles we can achieve nearly 100% tracking rate.