Data fusion of multisensor data for object identification in airport environment

Paper #:
  • 2017-01-2109

Published:
  • 2017-09-19
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Abstract:
Airport environment consists of several object movements both in air and on ground. In air objects include aircrafts, UAVs and birds etc. On ground objects include aircrafts, airport structures, ground vehicles and ground personnel etc. Detecting, classifying, identifying and tracking these objects are necessary for avoiding collisions in all environmental situations. Multiple sensors need to be employed for capturing the object shape and position from multiple directions. Data from these sensors are combined and processed for object identification. In current scenario, there is no comprehensive traffic monitoring system that uses multisensor data for monitoring in all the airport areas. In this paper, for explanation purpose, a hypothetical airport traffic monitoring system [1] is presumed that uses multiple sensors for avoiding collisions. Referenced system employs multi type sensors for object data collection in different situations, multi object data collected is combined to classify [2] and identify the objects, and identified objects are accurately tracked for collision prediction. This paper discusses about data fusion model [3] of multisensor data for object identification in an airport environment to allow the traffic monitoring system to determine shape, type and position of object. As a future scope of this paper, the object shape, type and position data from the object identification stage is provided as input to the next stage in the airport traffic monitoring system to track the object movements for collision prediction. Multiple type sensors are arranged in different configurations such as complementary, competitive and cooperative arrangement. Data from these sensors is combined for constructing the object and provide accurate identification. Optimal fusion model and object model mapping algorithm [4] are discussed for the object identification purpose. Simulation results of a case study on data fusion from video and other sensors are presented in this paper. Ref.:- [1] A methodology for collision prediction and alert generation in airport environment; Kiran Thupakula, Adishesha Sivaramasastry, Srikanth Gampa; [2] Classification of moving objects in atmospherically degraded video; Eli Chen, Oren Haik, Yitzhak Yitzhaky; [3] Multiple Sensor Fusion for Detection, Classification and Tracking of Moving Objects in Driving Environments; R. Omar Chavez-Garcia; [4] Shape-based object recognition in videos using 3D synthetic object models; Alexander Toshev, Ameesh Makadia, Kostas Daniilidis;
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