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18 January 2025 |
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Article overview
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Object-Centric Multiple Object Tracking | | Zixu Zhao
; Jiaze Wang
; Max Horn
; Yizhuo Ding
; Tong He
; Zechen Bai
; Dominik Zietlow
; Carl-Johann Simon-Gabriel
; Bing Shuai
; Zhuowen Tu
; Thomas Brox
; Bernt Schiele
; Yanwei Fu
; Francesco Locatello
; Zheng Zhang
; Tianjun Xiao
; | | Date: |
1 Sep 2023 | | Abstract: | Unsupervised object-centric learning methods allow the partitioning of scenes
into entities without additional localization information and are excellent
candidates for reducing the annotation burden of multiple-object tracking (MOT)
pipelines. Unfortunately, they lack two key properties: objects are often split
into parts and are not consistently tracked over time. In fact,
state-of-the-art models achieve pixel-level accuracy and temporal consistency
by relying on supervised object detection with additional ID labels for the
association through time. This paper proposes a video object-centric model for
MOT. It consists of an index-merge module that adapts the object-centric slots
into detection outputs and an object memory module that builds complete object
prototypes to handle occlusions. Benefited from object-centric learning, we
only require sparse detection labels (0%-6.25%) for object localization and
feature binding. Relying on our self-supervised
Expectation-Maximization-inspired loss for object association, our approach
requires no ID labels. Our experiments significantly narrow the gap between the
existing object-centric model and the fully supervised state-of-the-art and
outperform several unsupervised trackers. | | Source: | arXiv, 2309.00233 | | Services: | Forum | Review | PDF | Favorites | |
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