Abstract
Performance of a long term object tracker relies on the object detection accuracy. Although several object detectors are proposed in the literature, robustness to target disappearances and reappearances is still a challenging problem. To deal with this problem, we propose an inference pipeline that integrates an object detector with a meta-learner, both locally trained. This is achieved by replacing the head classification layer of the object detector by a meta-learner that also enables verification of the target. In particular, Mask R-CNN object detector is integrated with SDNet trained end-to-end for object tracking. Improvement achieved by MAML++ meta learner trained as a classifier is also evaluated. Numerical results reported on VOT2020-LT long term video dataset demonstrate that both SDNet and MAML++ meta-learners improve the detection accuracy for unseen object classes. Moreover verification by SDNET provides 7% increase on detection of target disappearance and reappearance frames.
Translated title of the contribution | Video Object Verification via Meta-learning |
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Original language | Turkish |
Title of host publication | 2022 30th Signal Processing and Communications Applications Conference, SIU 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781665450928 |
DOIs | |
Publication status | Published - 2022 |
Event | 30th Signal Processing and Communications Applications Conference, SIU 2022 - Safranbolu, Turkey Duration: 15 May 2022 → 18 May 2022 |
Publication series
Name | 2022 30th Signal Processing and Communications Applications Conference, SIU 2022 |
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Conference
Conference | 30th Signal Processing and Communications Applications Conference, SIU 2022 |
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Country/Territory | Turkey |
City | Safranbolu |
Period | 15/05/22 → 18/05/22 |
Bibliographical note
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