Object Tracking in Satellite Videos: A Spatial-Temporal Regularized Correlation Filter Tracking Method With Interacting Multiple Model
Yangfan Li, Chunjiang Bian
Abstract
Target occlusion is common in satellite videos, which makes object tracking difficult because most state-of-the-art trackers are not robust to occlusion, particularly complete occlusion. In this letter, we propose a novel correlation filter algorithm with an interacting multiple model (IMM) for object tracking in satellite videos that combines the strength of the correlation filter and the IMM. When the target is occluded, we utilize the IMM to predict target position. Therefore, the proposed tracker is robust to occlusion. The experimental results demonstrate that our tracker performs favorably when the target is occluded and achieves excellent performance compared with state-of-the-art methods.
Topics & Concepts
Artificial intelligenceComputer visionBitTorrent trackerTracking (education)Computer scienceVideo trackingFilter (signal processing)OcclusionSatellitePosition (finance)CorrelationObject (grammar)Eye trackingMathematicsEngineeringAerospace engineeringCardiologyEconomicsFinancePsychologyGeometryPedagogyMedicineVideo Surveillance and Tracking MethodsFire Detection and Safety SystemsIoT-based Smart Home Systems