Robust Visual Tracking With Spatial Regularization Kernelized Correlation Filter Constrained by a Learning Spatial Reliability Map
As a basic research topic in computer vision, visual tracking is still challenging because of the complexity of the tracking problems, such as abrupt motion, out-of-view, deformation, and heavy occlusion. In this paper, we extend the kernelized correlation filter (CF) for robust tracking by introduc...
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| Veröffentlicht in: | IEEE access 2019, Vol.7, p.27339-27351 |
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| creator | Liu, Qianbo Hu, Guoqing Islam, Md Mojahidul |
| description | As a basic research topic in computer vision, visual tracking is still challenging because of the complexity of the tracking problems, such as abrupt motion, out-of-view, deformation, and heavy occlusion. In this paper, we extend the kernelized correlation filter (CF) for robust tracking by introducing spatial regularization components to penalize the CF coefficients. To afford a more confident prediction, we construct a spatial reliability map based on the color histogram to enforce the detecting samples near the target center. The feature fusion and the model update mechanism are further employed to improve the effectiveness of tracking. The extensive experiments are executed on the OTB-2013, OTB-2015, and Temple Color-128 datasets. The comprehensive results demonstrate the superiority of our proposed method comparing to the representative tracks on these datasets. |
| doi_str_mv | 10.1109/ACCESS.2019.2902216 |
| format | Article |
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In this paper, we extend the kernelized correlation filter (CF) for robust tracking by introducing spatial regularization components to penalize the CF coefficients. To afford a more confident prediction, we construct a spatial reliability map based on the color histogram to enforce the detecting samples near the target center. The feature fusion and the model update mechanism are further employed to improve the effectiveness of tracking. The extensive experiments are executed on the OTB-2013, OTB-2015, and Temple Color-128 datasets. 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The comprehensive results demonstrate the superiority of our proposed method comparing to the representative tracks on these datasets.</description><subject>Color</subject><subject>Computer vision</subject><subject>Correlation</subject><subject>Datasets</subject><subject>Feature fusion</subject><subject>Histograms</subject><subject>Image color analysis</subject><subject>model update mechanism</subject><subject>Occlusion</subject><subject>Optical tracking</subject><subject>Regularization</subject><subject>Reliability</subject><subject>Robustness</subject><subject>spatial regularization components</subject><subject>spatial reliability map</subject><subject>Target detection</subject><subject>Target tracking</subject><subject>Visualization</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUU1v1DAQjRBIVG1_QS-WOO_ib8fHKmqhYiukboGj5cSTxYuJF9s5bI_8crykKngkf7yZ98b2a5orgteEYP3-uututts1xUSvqcaUEvmqOauzXjHB5Ov_9m-by5z3uI62QkKdNb8fYj_ngr76PNuAHpMdfvhph7758h1tD7b4ij7Abg42-ad6jBP6BGmC4J_AoS6mBGGBb30okCo05ZKsn2q6PyKLNmDTdNL8Jxe87X3w5Yju7eGieTPakOHyeT1vvtzePHYfV5vPH-66681q4KItKwpqoKJXrG8pc0CU1URzrsZeC64kxoIPTFMycqYYVjUAt5hJOWAnLIzsvLlbdF20e3NI_qdNRxOtN3-BmHbGpuKHAAZzzuXogDvleCvrVznCiZOajJSN_VC13i1ahxR_zZCL2cc5TfX6hnIhJFNKylrFlqohxZwTjC9dCTYn78zinTl5Z569q6yrheUB4IXRSsHrI9kfy0OVPQ</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Liu, Qianbo</creator><creator>Hu, Guoqing</creator><creator>Islam, Md Mojahidul</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals |
| subjects | Color Computer vision Correlation Datasets Feature fusion Histograms Image color analysis model update mechanism Occlusion Optical tracking Regularization Reliability Robustness spatial regularization components spatial reliability map Target detection Target tracking Visualization |
| title | Robust Visual Tracking With Spatial Regularization Kernelized Correlation Filter Constrained by a Learning Spatial Reliability Map |
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