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Data Fusion Techniques for Object Identification in Airport Environment
ISSN: 0148-7191, e-ISSN: 2688-3627
Published September 19, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Airport environments consist of several moving objects both in the air and on the ground. In air moving objects include aircraft, UAVs and birds etc. On ground moving objects include aircraft, 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 purposes, a hypothetical airport traffic monitoring system is presumed that uses multiple sensors for avoiding collisions. The referenced system employs multiple types of sensors for object data collection in different situations, wherein the collected multi object data is combined to classify and identify the objects, and identified objects are accurately tracked for collision prediction.
This paper discusses a data fusion model of multisensor data for object identification in an airport environment to allow the traffic monitoring system to determine the shape, type and position of an 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 arrangements. Data from these sensors is combined for object detection and identification. Optimal fusion model and object model mapping algorithms are discussed for the object identification purpose. A case study of the competitive sensor data fusion is also discussed in this paper.
CitationThupakula, K., "Data Fusion Techniques for Object Identification in Airport Environment," SAE Technical Paper 2017-01-2109, 2017, https://doi.org/10.4271/2017-01-2109.
Data Sets - Support Documents
|Unnamed Dataset 1|
- A methodology for collision prediction and alert generation in airport environment Thupakula Kiran , Sivaramasastry Adishesha , Gampa Srikanth
- Concepts and Theory of Data Fusion Multi-Sensor Data Fusion with MATLAB Rao Jitendra R CRC Press
- Multiple Sensor Fusion for Detection, Classification and Tracking of Moving Objects in Driving Environments Chavez-Garcia R. Omar
- Shape-based object recognition in videos using 3D synthetic object models Toshev Alexander , Makadia Ameesh , Daniilidis Kostas
- Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems Oh Sang-Il and Kang Hang-Bong Department of Media Engineering, Catholic University of Korea