Summary of Heterogeneous Graph Transformer For Multiple Tiny Object Tracking in Rgb-t Videos, by Qingyu Xu et al.
Heterogeneous Graph Transformer for Multiple Tiny Object Tracking in RGB-T Videos
by Qingyu Xu, Longguang Wang, Weidong Sheng, Yingqian Wang, Chao Xiao, Chao Ma, Wei An
First submitted to arxiv on: 14 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 In this paper, the authors propose a novel framework called HGT-Track for tracking multiple tiny objects. The existing multi-object tracking algorithms are limited to single-modality scenes and do not account for the complementary characteristics of tiny objects captured by multiple remote sensors. To address this challenge, the authors employ a Transformer-based encoder to embed images from different modalities, followed by a Heterogeneous Graph Transformer to aggregate spatial and temporal information. A target re-detection module (ReDet) is introduced to ensure tracklet continuity across different modalities. The paper also introduces the VT-Tiny-MOT benchmark for RGB-T fused multiple tiny object tracking. Extensive experiments demonstrate the effectiveness of the proposed method, achieving better performance compared to state-of-the-art methods in terms of MOTA and ID-F1 score. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research proposes a new way to track many small objects. Currently, algorithms that track multiple objects focus on scenes with just one type of data (like cameras). But real-world scenarios often involve different types of sensors, like thermal imaging. The authors create a framework called HGT-Track that combines information from these different sources to improve tracking accuracy. They also develop a special module to ensure the tracks stay consistent across different sensors. This method performs better than other top methods in tracking many small objects. |
Keywords
» Artificial intelligence » Encoder » F1 score » Object tracking » Tracking » Transformer
