Summary of Into the Fog: Evaluating Robustness Of Multiple Object Tracking, by Nadezda Kirillova et al.
Into the Fog: Evaluating Robustness of Multiple Object Tracking
by Nadezda Kirillova, M. Jehanzeb Mirza, Horst Bischof, Horst Possegger
First submitted to arxiv on: 12 Apr 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 This paper introduces a novel method for simulating fog in Multiple Object Tracking (MOT) datasets, allowing for the evaluation of trackers’ performance in adverse atmospheric conditions. By leveraging frame-by-frame monocular depth estimation and a physics-based fog formation model, the proposed simulation can render both homogeneous and heterogeneous fog. This methodology is shown to improve MOT methods’ robustness even in night and indoor scenes. The paper presents the third release of the MOTChallenge benchmark augmented with fog and conducts a comprehensive evaluation of various MOT methods, revealing their limitations under fog-like challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how objects are tracked in different weather conditions. Current tracking systems work well when it’s clear outside, but what happens when there’s fog, smoke, or dust? The authors created a new way to simulate these conditions in videos so we can test how well the trackers work. They showed that by using this method, we can make trackers better at handling tricky weather. This is important because it helps us create systems that can track objects accurately even when the environment is challenging. |
Keywords
» Artificial intelligence » Depth estimation » Object tracking » Tracking
