Dynamic Template Selection Through Change Detection for Adaptive Siamese Tracking
Madhu Kiran, Le Thanh Nguyen-Meidine, Rajat Sahay, Rafael Menelau, Oliveira E Cruz, Louis-Antoine Blais-Morin, Eric Granger

TL;DR
This paper introduces a change detection-based method for dynamic template selection in adaptive Siamese trackers, improving their robustness and accuracy by preventing template corruption during online learning.
Contribution
It proposes a novel change detection mechanism and entropy-based sample selection strategy for online adaptation in Siamese tracking, enhancing performance and preventing template corruption.
Findings
Improved tracking accuracy on multiple datasets.
Enhanced robustness against target appearance changes.
Effective online adaptation with reduced template corruption.
Abstract
Deep Siamese trackers have recently gained much attention in recent years since they can track visual objects at high speeds. Additionally, adaptive tracking methods, where target samples collected by the tracker are employed for online learning, have achieved state-of-the-art accuracy. However, single object tracking (SOT) remains a challenging task in real-world application due to changes and deformations in a target object's appearance. Learning on all the collected samples may lead to catastrophic forgetting, and thereby corrupt the tracking model. In this paper, SOT is formulated as an online incremental learning problem. A new method is proposed for dynamic sample selection and memory replay, preventing template corruption. In particular, we propose a change detection mechanism to detect gradual changes in object appearance and select the corresponding samples for online…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVideo Surveillance and Tracking Methods · Fire Detection and Safety Systems · Remote-Sensing Image Classification
