Moving object detection for unconstrained low-altitude aerial videos, a pose-independent detector based on artificial flow

Thomas Castelli; Alain Trémeau; Hubert Konik; Eric Dinet

Moving object detection for unconstrained low-altitude aerial videos, a pose-independent detector based on artificial flow

Thomas Castelli Alain Trémeau ¹ Hubert Konik ¹ Eric Dinet ¹

1 LHC - Laboratoire Hubert Curien [Saint Etienne]

Abstract : Automatic detection of moving objects is an important task for aerial surveillance. It has been a popular and well-studied subject for the computer vision community, but is still a challenge. The method we introduce targets surveillance low-altitude mini and micro-UAVs. We take advantage of the inherent image motion on footage captured by such aerial vehicles. Our method confronts Optical Flow vectors and an estimated Flow in order to detect independently moving pixels. This motion-based approach is robust to operational conditions and to the geometric properties of the scene. The efficiency of the method was computed on the VIVID database. The moving areas detected will make the tracking task more robust and efficient.

Document type :

Conference papers

Domain :

Computer Science [cs] / Computer Vision and Pattern Recognition [cs.CV]

Complete list of metadatas

https://hal-ujm.archives-ouvertes.fr/ujm-01388681
Contributor : Eric Dinet <>
Submitted on : Thursday, October 27, 2016 - 1:49:37 PM
Last modification on : Thursday, July 26, 2018 - 1:11:23 AM

Moving object detection for unconstrained low-altitude aerial videos, a pose-independent detector based on artificial flow

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Moving object detection for unconstrained low-altitude aerial videos, a pose-independent detector based on artificial flow

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