Learning Rotation Adaptive Correlation Filters in Robust Visual Object Tracking
| Autor: | Priya Mariam Raju, Rama Krishna Sai Subrahmanyam Gorthi, Litu Rout, Deepak Mishra |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry BitTorrent tracker Correlation filter 020206 networking & telecommunications 02 engineering and technology Correlation Discriminative model Robustness (computer science) Video tracking 0202 electrical engineering electronic engineering information engineering False positive paradox 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | Computer Vision – ACCV 2018 ISBN: 9783030208899 ACCV (2) |
| Popis: | Visual object tracking is one of the major challenges in the field of computer vision. Correlation Filter (CF) trackers are one of the most widely used categories in tracking. Though numerous tracking algorithms based on CFs are available today, most of them fail to efficiently detect the object in an unconstrained environment with dynamically changing object appearance. In order to tackle such challenges, the existing strategies often rely on a particular set of algorithms. Here, we propose a robust framework that offers the provision to incorporate illumination and rotation invariance in the standard Discriminative Correlation Filter (DCF) formulation. We also supervise the detection stage of DCF trackers by eliminating false positives in the convolution response map. Further, we demonstrate the impact of displacement consistency on CF trackers. The generality and efficiency of the proposed framework is illustrated by integrating our contributions into two state-of-the-art CF trackers: SRDCF and ECO. As per the comprehensive experiments on the VOT2016 dataset, our top trackers show substantial improvement of \(14.7\%\) and \(6.41\%\) in robustness, \(11.4\%\) and \(1.71\%\) in Average Expected Overlap (AEO) over the baseline SRDCF and ECO, respectively. |
| Databáze: | OpenAIRE |
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