1. Adaptive Birth for the GLMB Filter for Object Tracking in Satellite Videos
- Author
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Aguilar, Camilo, Ortner, Mathias, Zerubia, Josiane, Télédetection et IA embarqués pour le 'New Space' (AYANA), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Côte d'Azur (UCA), AIRBUS DS (Toulouse), and ANR-15-IDEX-0001,UCA JEDI,Idex UCA JEDI(2015)
- Subjects
GLMB ,remote sensing ,RFS ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ,multitarget tracking ,adaptive birth - Abstract
International audience; The Generalized Labeled Multi-Bernoulli (GLMB) filter attains remarkable results in Multi-Object Tracking (MOT). Nevertheless, the GLMB filter relies on strong assumptions such as prior knowledge of targets' initial state. Pragmatic scenarios such as satellite video object tracking challenge these assumptions as objects appear at random locations and object detectors output numerous false positives. We present an enhanced version of the GLMB filter that learns from previous trajectories to estimate accurate hypotheses initializations. We keep track of previous target states and use this information to sample the initial velocities of newborn targets. This addition significantly improves the performance of the GLMB in videos with low Frames Per Second (FPS), where the target's initial states are paramount for object tracking. We test this enhanced GLMB filter versus comparable trackers and previous solutions for the GLMB filter and show that our filter obtains better performance. Code is available at https://github.com/Ayana-Inria/GLMB-adaptivebirth-satellite-videos
- Published
- 2022