ELTrack: Events-Language Description for Visual Object Tracking
The integration of Natural Language (NL) descriptions into Visual Object Tracking (VOT) has shown promise in enhancing the performance of RGB-based tracking by providing richer, contextually aware information that helps to address issues like appearance variations, model drift, and ambiguous target...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10879396/ |
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| author | Mohamad Alansari Khaled Alnuaimi Sara Alansari Sajid Javed |
| author_facet | Mohamad Alansari Khaled Alnuaimi Sara Alansari Sajid Javed |
| author_sort | Mohamad Alansari |
| collection | DOAJ |
| description | The integration of Natural Language (NL) descriptions into Visual Object Tracking (VOT) has shown promise in enhancing the performance of RGB-based tracking by providing richer, contextually aware information that helps to address issues like appearance variations, model drift, and ambiguous target representation. However, the growing complexity of VOT tasks, particularly in scenarios involving fast-moving objects and challenging lighting conditions, necessitates the development of more robust and adaptable tracking frameworks. Traditional visual trackers, which rely solely on RGB data, often struggle with these challenges. Event cameras offer promising solutions that capture changes in a scene as they happen because of their reduced latency, making them highly effective in scenarios where traditional visible cameras often struggle, such as in low-light environments or when tracking rapid motion. Despite the process in events and NL tracking, the fusion of events and NL remains underexplored due to the lack of large-scale NL-described datasets and event-based benchmarks. To address these gaps, we present ELTrack, a novel multi-modal NL-based VOT framework that, to the best of our knowledge, is the first to integrate event data with NL descriptions in VOT. ELTrack synthesizes event data, filters out noise, and applies imprinting and a step decay function to introduce a novel event image representation called Pseudo-Frames. Additionally, we generate NL descriptions using a Visual-Language (VL) image-captioning module featuring BLIP-2 and GPT-4. These modalities are seamlessly integrated using a superimpose fusion module to enhance tracking performance. Our ELTrack is a generic pipeline and can be integrated with any of the existing SoTA trackers. Extensive experiments demonstrate that ELTrack achieves significantly better performance across a variety of publicly available VOT datasets. The source code of the ELTrack is publicly available at: <uri>https://github.com/HamadYA/ELTrack-Correlating-Events-and-Language-for-Visual-Tracking</uri>. |
| format | Article |
| id | doaj-art-9caeda33c57a4da1916de62f0627a6e8 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-9caeda33c57a4da1916de62f0627a6e82025-08-20T02:15:29ZengIEEEIEEE Access2169-35362025-01-0113313513136710.1109/ACCESS.2025.354044510879396ELTrack: Events-Language Description for Visual Object TrackingMohamad Alansari0https://orcid.org/0000-0003-2960-2972Khaled Alnuaimi1Sara Alansari2Sajid Javed3https://orcid.org/0000-0002-0036-2875Department of Computer Science, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Computer Science, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Computer Science, Khalifa University, Abu Dhabi, United Arab EmiratesDepartment of Computer Science, Khalifa University, Abu Dhabi, United Arab EmiratesThe integration of Natural Language (NL) descriptions into Visual Object Tracking (VOT) has shown promise in enhancing the performance of RGB-based tracking by providing richer, contextually aware information that helps to address issues like appearance variations, model drift, and ambiguous target representation. However, the growing complexity of VOT tasks, particularly in scenarios involving fast-moving objects and challenging lighting conditions, necessitates the development of more robust and adaptable tracking frameworks. Traditional visual trackers, which rely solely on RGB data, often struggle with these challenges. Event cameras offer promising solutions that capture changes in a scene as they happen because of their reduced latency, making them highly effective in scenarios where traditional visible cameras often struggle, such as in low-light environments or when tracking rapid motion. Despite the process in events and NL tracking, the fusion of events and NL remains underexplored due to the lack of large-scale NL-described datasets and event-based benchmarks. To address these gaps, we present ELTrack, a novel multi-modal NL-based VOT framework that, to the best of our knowledge, is the first to integrate event data with NL descriptions in VOT. ELTrack synthesizes event data, filters out noise, and applies imprinting and a step decay function to introduce a novel event image representation called Pseudo-Frames. Additionally, we generate NL descriptions using a Visual-Language (VL) image-captioning module featuring BLIP-2 and GPT-4. These modalities are seamlessly integrated using a superimpose fusion module to enhance tracking performance. Our ELTrack is a generic pipeline and can be integrated with any of the existing SoTA trackers. Extensive experiments demonstrate that ELTrack achieves significantly better performance across a variety of publicly available VOT datasets. The source code of the ELTrack is publicly available at: <uri>https://github.com/HamadYA/ELTrack-Correlating-Events-and-Language-for-Visual-Tracking</uri>.https://ieeexplore.ieee.org/document/10879396/Events cameramulti-modal fusionneuromorphic visionvisual-language object trackingvisual object tracking (VOT) |
| spellingShingle | Mohamad Alansari Khaled Alnuaimi Sara Alansari Sajid Javed ELTrack: Events-Language Description for Visual Object Tracking IEEE Access Events camera multi-modal fusion neuromorphic vision visual-language object tracking visual object tracking (VOT) |
| title | ELTrack: Events-Language Description for Visual Object Tracking |
| title_full | ELTrack: Events-Language Description for Visual Object Tracking |
| title_fullStr | ELTrack: Events-Language Description for Visual Object Tracking |
| title_full_unstemmed | ELTrack: Events-Language Description for Visual Object Tracking |
| title_short | ELTrack: Events-Language Description for Visual Object Tracking |
| title_sort | eltrack events language description for visual object tracking |
| topic | Events camera multi-modal fusion neuromorphic vision visual-language object tracking visual object tracking (VOT) |
| url | https://ieeexplore.ieee.org/document/10879396/ |
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