Summary of Enhanced Object Tracking by Self-supervised Auxiliary Depth Estimation Learning, By Zhenyu Wei et al.
Enhanced Object Tracking by Self-Supervised Auxiliary Depth Estimation Learning
by Zhenyu Wei, Yujie He, Zhanchuan Cai
First submitted to arxiv on: 23 May 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 proposed MDETrack method improves RGB-D tracking by incorporating depth information through supervised or self-supervised auxiliary Monocular Depth Estimation learning. The unified feature extractor provides outputs to both the side-by-side tracking head and auxiliary depth estimation head, with the latter being discarded in inference for identical inference speed. Experimental results demonstrate improved tracking accuracy across multiple datasets without requiring real depth inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RGB-D tracking helps track objects more accurately, but it’s limited by needing real depth information. The new MDETrack method trains a network that can understand scene depth, making object tracking better even without actual depth. This is achieved through learning from supervised or self-supervised data. The result is a faster and more accurate way to track objects. |
Keywords
» Artificial intelligence » Depth estimation » Inference » Object tracking » Self supervised » Supervised » Tracking
