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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 2: AAAI-22 Technical Tracks 2

One More Check: Making “Fake Background” Be Tracked Again

February 1, 2023

Authors

Chao Liang

School of Automation Engineering, University of Electronic Science and Technology of China (UESTC)


Zhipeng Zhang

NLPR, Institute of Automation, Chinese Academy of Sciences (CASIA)


Xue Zhou

School of Automation Engineering, University of Electronic Science and Technology of China (UESTC); Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China (UESTC); Intelligent Terminal Key Laboratory of SiChuan Province


Bing Li

NLPR, Institute of Automation, Chinese Academy of Sciences (CASIA)


Weiming Hu

NLPR, Institute of Automation, Chinese Academy of Sciences (CASIA)


Proceedings:

No. 2: AAAI-22 Technical Tracks 2

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Computer Vision II

Downloads:

Download PDF

Abstract:

The one-shot multi-object tracking, which integrates object detection and ID embedding extraction into a unified network, has achieved groundbreaking results in recent years. However, current one-shot trackers solely rely on single-frame detections to predict candidate bounding boxes, which may be unreliable when facing disastrous visual degradation, e.g., motion blur, occlusions. Once a target bounding box is mistakenly classified as background by the detector, the temporal consistency of its corresponding tracklet will be no longer maintained. In this paper, we set out to restore the bounding boxes misclassified as ``fake background'' by proposing a re-check network. The re-check network innovatively expands the role of ID embedding from data association to motion forecasting by effectively propagating previous tracklets to the current frame with a small overhead. Note that the propagation results are yielded by an independent and efficient embedding search, preventing the model from over-relying on detection results. Eventually, it helps to reload the ``fake background'' and repair the broken tracklets. Building on a strong baseline CSTrack, we construct a new one-shot tracker and achieve favorable gains by 70.7 ➡ 76.4, 70.6 ➡ 76.3 MOTA on MOT16 and MOT17, respectively. It also reaches a new state-of-the-art MOTA and IDF1 performance. Code is released at https://github.com/JudasDie/SOTS.

DOI:

10.1609/aaai.v36i2.20045


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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