From Certain to Uncertain: Toward Optimal Solution for Offline Multiple Object Tracking
| Autor: | Hiroshi Mouri, Tetsu Matsukawa, Einoshin Suzuki, Takashi Imaseki, Kaikai Zhao |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Similarity (geometry) Computer science Intersection (set theory) business.industry 02 engineering and technology Kalman filter Tracking (particle physics) Object (computer science) computer.software_genre 020901 industrial engineering & automation Video tracking Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering Code (cryptography) 020201 artificial intelligence & image processing Data mining Artificial intelligence business computer |
| Zdroj: | ICPR |
| DOI: | 10.1109/icpr48806.2021.9413215 |
| Popis: | Affinity measure in object tracking outputs a similarity or distance score for given detections. As an affinity measure is typically imperfect, it generally has an uncertain region in which regarding two groups of detections as the same object or different objects based on the score can be wrong. How to reduce the uncertain region is a major challenge for most similarity-based tracking methods. Early mistakes often result in distribution drifts for tracked objects and this is another major issue for object tracking. In this paper, we propose a new offline tracking method called agglomerative hierarchical clustering with ensemble of tracking experts (AHC_ETE), to tackle the uncertain region and early mistake issues. We conduct tracking from certain to uncertain to reduce early mistakes. Meanwhile, we ensemble multiple tracking experts to reduce the uncertain region as the final uncertain region is the intersection of those of all tracking experts. Experiments on the MOT15 and MOT16 datasets demonstrated the effectiveness of our method. The code is publicly available at https://github.com/cyoukaikai/ahc_ete. |
| Databáze: | OpenAIRE |
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