Global Motion-Aware Robust Visual Object Tracking for Electro Optical Targeting Systems

Although recently developed trackers have shown excellent performance even when tracking fast moving and shape changing objects with variable scale and orientation, the trackers for the electro-optical targeting systems (EOTS) still suffer from abrupt scene changes due to frequent and fast camera mo...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-01, Vol.20 (2), p.566
Main Authors: Kim, Byeong Hak, Lukezic, Alan, Lee, Jong Hyuk, Jung, Ho Min, Kim, Min Young
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
camera motion
Cameras
Datasets
Deep learning
Embedded systems
Infrared imagery
Localization
Methods
motion aware
Motion perception
robust tracking
Semantics
Sensors
visual object tracking
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Summary:Although recently developed trackers have shown excellent performance even when tracking fast moving and shape changing objects with variable scale and orientation, the trackers for the electro-optical targeting systems (EOTS) still suffer from abrupt scene changes due to frequent and fast camera motions by pan-tilt motor control or dynamic distortions in field environments. Conventional context aware (CA) and deep learning based trackers have been studied to tackle these problems, but they have the drawbacks of not fully overcoming the problems and dealing with their computational burden. In this paper, a global motion aware method is proposed to address the fast camera motion issue. The proposed method consists of two modules: (i) a motion detection module, which is based on the change in image entropy value, and (ii) a background tracking module, used to track a set of features in consecutive images to find correspondences between them and estimate global camera movement. A series of experiments is conducted on thermal infrared images, and the results show that the proposed method can significantly improve the robustness of all trackers with a minimal computational overhead. We show that the proposed method can be easily integrated into any visual tracking framework and can be applied to improve the performance of EOTS applications.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20020566