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• W3039969228 abstract "Visual object tracking (VOT) is an essential component for many applications, such as autonomous driving or assistive robotics. However, recent works tend to develop accurate systems based on more computationally expensive feature extractors for better instance matching. In contrast, this work addresses the importance of motion prediction in VOT. We use an off-the-shelf object detector to obtain instance bounding boxes. Then, a combination of camera motion decouple and Kalman filter is used for state estimation. Although our baseline system is a straightforward combination of standard methods, we obtain state-of-the-art results. Our method establishes new state-of-the-art performance on VOT (VOT-2016 and VOT-2018). Our proposed method improves the EAO on VOT-2016 from 0.472 of prior art to 0.505, from 0.410 to 0.431 on VOT-2018. To show the generalizability, we also test our method on video object segmentation (VOS: DAVIS-2016 and DAVIS-2017) and observe consistent improvement." @default.
• W3039969228 created "2020-07-10" @default.
• W3039969228 creator A5037075675 @default.
• W3039969228 creator A5076586872 @default.
• W3039969228 date "2020-07-01" @default.
• W3039969228 modified "2023-09-23" @default.
• W3039969228 title "Motion Prediction in Visual Object Tracking" @default.
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• W3039969228 doi "https://doi.org/10.48550/arxiv.2007.01120" @default.
• W3039969228 hasPublicationYear "2020" @default.
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