A new skeleton based flying bird detection method for low-altitude air traffic management
| Autor: | Qunyu Xu, Tianhang Wu, Xiaoyan Luo |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Low altitude
0209 industrial biotechnology Computer science business.industry Aviation Mechanical Engineering Air traffic management Aerospace Engineering TL1-4050 02 engineering and technology Skeleton (category theory) GeneralLiterature_MISCELLANEOUS Set (abstract data type) Consistency (database systems) 020901 industrial engineering & automation Feature (computer vision) 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Motor vehicles. Aeronautics. Astronautics ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | Chinese Journal of Aeronautics, Vol 31, Iss 11, Pp 2149-2164 (2018) |
| ISSN: | 1000-9361 |
| Popis: | In low-altitude air traffic management, non-cooperation targets are the greatest threat to security of low-flying aircraft. Among various aviation fatalities, flying bird is the main factor with the highest risk and directs economic losses amounted to nearly 10 billion US dollars each year. Therefore, Flying Bird Detection (FBD) has attracted considerable attention in low-altitude air traffic management. In this paper, we propose a skeleton based FBD method via describing bird motion information with a set of key poses. To overcome the variability of birds, the skeleton feature is selected as a relatively fixed and common characteristic for the pose appearance of flying bird. Based on the geometric topology among some key parts of bird body, a set of key poses can be described by some extracted skeleton features, which are used to represent the bird motion information. Aimed at robustly handling with the pose variations, multiple pose-specific classifiers are individually trained to learn the representative poses of the flying bird. At the detection stage, the flying bird skeleton features are combined with extracted key-pose sets to perform the flying bird classification task from each image. Afterwards, the key-frame pose-change set and the consistency of the classification results from sequent images are employed to validate the final detection results. Experiments on flying bird datasets demonstrate the effectiveness and efficiency of the proposed method. Keywords: Bird-aircraft collisions, Flight security, Flying bird detection, Low-Altitude Air Traffic Management (LAATM), Simplified skeleton descriptor |
| Databáze: | OpenAIRE |
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