Summary of Visual Object Tracking Across Diverse Data Modalities: a Review, by Mengmeng Wang et al.
Visual Object Tracking across Diverse Data Modalities: A Review
by Mengmeng Wang, Teli Ma, Shuo Xin, Xiaojun Hou, Jiazheng Xing, Guang Dai, Jingdong Wang, Yong Liu
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive survey of recent progress in Visual Object Tracking (VOT), focusing on both single-modal and multi-modal approaches using deep learning methods. The authors review three mainstream single-modal VOT types: RGB, thermal infrared, and point cloud tracking. They conclude four widely-used single-modal frameworks, abstracting their schemas and categorizing existing inheritors. The paper also summarizes four kinds of multi-modal VOT: RGB-Depth, RGB-Thermal, RGB-LiDAR, and RGB-Language. Benchmark comparisons are presented for the discussed modalities. Recommendations and insightful observations are provided, inspiring future development in this fast-growing literature. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Visual Object Tracking (VOT) is a way to recognize and follow objects in videos without knowing what they are. This technology could be used in many situations, like tracking people or animals in different environments. The paper looks at how far we’ve come in making computers better at VOT using deep learning methods. It covers three main types of single-modal VOT (video, infrared, and 3D point cloud) and four popular frameworks that work well together. The authors also discuss multi-modal VOT, which combines different sensors like cameras and lidars to track objects. They compare the performance of these approaches on different datasets and provide some advice for improving this field. |
Keywords
» Artificial intelligence » Deep learning » Multi modal » Object tracking » Tracking
