Tracking Failure Prediction for Siamese Trackers Based on Channel Feature Statistics
- Authors
- Lee, K.; Do, H.; Ha, T.; Choi, Jongwon; Choi, J.Y.
- Issue Date
- Dec-2022
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Citation
- AVSS 2022 - 18th IEEE International Conference on Advanced Video and Signal-Based Surveillance
- Journal Title
- AVSS 2022 - 18th IEEE International Conference on Advanced Video and Signal-Based Surveillance
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/61134
- DOI
- 10.1109/AVSS56176.2022.9959442
- ISSN
- 0000-0000
- Abstract
- Failure prediction has rarely been studied for Siamese trackers due to a lack of meaningful analysis of tracking failing cases. In this paper, we provide a meaningful analysis of tracking failure in Siamese trackers. Our analysis includes the statistics of the channel-wise feature correlation between the exemplar and tracked target patches. We observe that the correlation statistics (max, mean, and std) are highly related to the overlapping ratio between tracked and ground-truth bounding boxes. Based on this observation, we devise a tracking failure prediction model that extracts more plentiful factors than simple statistics. The proposed tracking failure prediction model is validated on most-popular tracking benchmark datasets through extensive experiments. © 2022 IEEE.
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Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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