Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912087658201088 |
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| author | Shi, Kun He, Shibo Shi, Zhenyu Chen, Anjun Xiong, Zehui Chen, Jiming Luo, Jun |
| author_facet | Shi, Kun He, Shibo Shi, Zhenyu Chen, Anjun Xiong, Zehui Chen, Jiming Luo, Jun |
| contents | Multi-modal fusion is imperative to the implementation of reliable object detection and tracking in complex environments. Exploiting the synergy of heterogeneous modal information endows perception systems the ability to achieve more comprehensive, robust, and accurate performance. As a nucleus concern in wireless-vision collaboration, radar-camera fusion has prompted prospective research directions owing to its extensive applicability, complementarity, and compatibility. Nonetheless, there still lacks a systematic survey specifically focusing on deep fusion of radar and camera for object detection and tracking. To fill this void, we embark on an endeavor to comprehensively review radar-camera fusion in a holistic way. First, we elaborate on the fundamental principles, methodologies, and applications of radar-camera fusion perception. Next, we delve into the key techniques concerning sensor calibration, modal representation, data alignment, and fusion operation. Furthermore, we provide a detailed taxonomy covering the research topics related to object detection and tracking in the context of radar and camera technologies.Finally, we discuss the emerging perspectives in the field of radar-camera fusion perception and highlight the potential areas for future research. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_19872 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey Shi, Kun He, Shibo Shi, Zhenyu Chen, Anjun Xiong, Zehui Chen, Jiming Luo, Jun Computer Vision and Pattern Recognition Multi-modal fusion is imperative to the implementation of reliable object detection and tracking in complex environments. Exploiting the synergy of heterogeneous modal information endows perception systems the ability to achieve more comprehensive, robust, and accurate performance. As a nucleus concern in wireless-vision collaboration, radar-camera fusion has prompted prospective research directions owing to its extensive applicability, complementarity, and compatibility. Nonetheless, there still lacks a systematic survey specifically focusing on deep fusion of radar and camera for object detection and tracking. To fill this void, we embark on an endeavor to comprehensively review radar-camera fusion in a holistic way. First, we elaborate on the fundamental principles, methodologies, and applications of radar-camera fusion perception. Next, we delve into the key techniques concerning sensor calibration, modal representation, data alignment, and fusion operation. Furthermore, we provide a detailed taxonomy covering the research topics related to object detection and tracking in the context of radar and camera technologies.Finally, we discuss the emerging perspectives in the field of radar-camera fusion perception and highlight the potential areas for future research. |
| title | Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.19872 |