Intelligent Transport Systems. From Research and Development to the Market Uptake. Third EAI International Conference, INTSYS 2019, Braga, Portugal, December 4–6, 2019

Research Article

Tracking and Classification of Aerial Objects

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  • @INPROCEEDINGS{10.1007/978-3-030-38822-5_18,
        author={Marcia Baptista and Luis Fernandes and Paulo Chaves},
        title={Tracking and Classification of Aerial Objects},
        proceedings={Intelligent Transport Systems. From Research and Development to the Market Uptake. Third EAI International Conference, INTSYS 2019, Braga, Portugal, December 4--6, 2019},
        proceedings_a={INTSYS},
        year={2020},
        month={1},
        keywords={Object tracking Deep learning Residual networks},
        doi={10.1007/978-3-030-38822-5_18}
    }
    
  • Marcia Baptista
    Luis Fernandes
    Paulo Chaves
    Year: 2020
    Tracking and Classification of Aerial Objects
    INTSYS
    Springer
    DOI: 10.1007/978-3-030-38822-5_18
Marcia Baptista1,*, Luis Fernandes1,*, Paulo Chaves1,*
  • 1: INOV Inesc Inovacao
*Contact email: marcia.baptista@inov.pt, luis.fernandes@inov.pt, paulo.chaves@inov.pt

Abstract

Unauthorized drone flying can prompt disruptions in critical facilities such as airports or railways. To prevent these situations, we propose a surveillance system that can sense malicious and/or illicit aerial targets. The idea is to track moving aerial objects using a static camera and when a tracked object is considered suspicious, the camera zooms in to take a snapshot of the target. This snapshot is then classified as an aircraft, drone, bird or cloud. In this work, we propose the classical technique of two-frame background subtraction to detect moving objects. We use the discrete Kalman filter to predict the location of each object and the Jonker-Volgenant algorithm to match objects between consecutive image frames. A deep residual network, trained with transfer learning, is used for image classification. The residual net ResNet-50 developed for the ILSVRC competition was retrained for this purpose. The performance of the system was evaluated with positive results in real-world conditions. The system was able to track multiple aerial objects with acceptable accuracy and the classification system also exhibited high performance.