The DOI matches its Crossref record; 88 of the 88 references it cites were found in a registry or an index.
Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies
Open accessAbstract
The majority of existing solutions to the Multi-Target Tracking (MTT) problem do not combine cues over a long period of time in a coherent fashion. In this paper, we present an online method that encodes long-term temporal dependencies across multiple cues. One key challenge of tracking methods is to accurately track occluded targets or those which share similar appearance properties with surrounding objects. To address this challenge, we present a structure of Recurrent Neural Networks (RNN) that jointly reasons on multiple cues over a temporal window. Our method allows to correct data association errors and recover observations from occluded states. We demonstrate the robustness of our data-driven approach by tracking multiple targets using their appearance, motion, and even interactions. Our method outperforms previous works on multiple publicly available datasets including the challenging MOT benchmark.
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Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
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Self-citationPerformance Measures and a Data Set for Multi-target, Multi-camera Tracking
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Detection and tracking meet drones challenge
Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., & Ling, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3119563 · Cited by 1,336
Deep learning for cellular image analysis
Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., & Van Valen, D. (2019) · Nature Methods · doi:10.1038/s41592-019-0403-1 · Cited by 1,225
Tracking Without Bells and Whistles
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Metadata
- Authors
- Sadeghian, A., Alahi, A., & Savarese, S.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 300-311
- OpenAlex
- W2579024533
- Cited by
- 553
- References
- 88
- FWCI
- 22.97
- Type
- conference paper
- Language
- en
- Open access
- green
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- Sadeghian, A., Alahi, A., & Savarese, S. (2017). Tracking the Untrackable: Learning to Track Multiple Cues with Long-Term Dependencies. 2017 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2017.41
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