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Fast Visual Object Tracking with Rotated Bounding Boxes

2019-07-08Code Available0· sign in to hype

Bao Xin Chen, John K. Tsotsos

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Abstract

In this paper, we demonstrate a novel algorithm that uses ellipse fitting to estimate the bounding box rotation angle and size with the segmentation(mask) on the target for online and real-time visual object tracking. Our method, SiamMask_E, improves the bounding box fitting procedure of the state-of-the-art object tracking algorithm SiamMask and still retains a fast-tracking frame rate (80 fps) on a system equipped with GPU (GeForce GTX 1080 Ti or higher). We tested our approach on the visual object tracking datasets (VOT2016, VOT2018, and VOT2019) that were labeled with rotated bounding boxes. By comparing with the original SiamMask, we achieved an improved Accuracy of 0.652 and 0.309 EAO on VOT2019, which is 0.056 and 0.026 higher than the original SiamMask. The implementation is available on GitHub: https://github.com/baoxinchen/siammask_e.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
VOT2016SiamMask_EExpected Average Overlap (EAO)0.47Unverified
VOT2017/18SiamMask_EExpected Average Overlap (EAO)0.45Unverified
VOT2019SiamMask_EExpected Average Overlap (EAO)0.31Unverified

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