Navaneet, KL and Sarvadevabhatla, RK and Shekhar, S and Venkatesh Babu, R and Chakraborty, A (2020) Operator-in-the-Loop Deep Sequential Multi-Camera Feature Fusion for Person Re-Identification. In: IEEE Transactions on Information Forensics and Security, 15 . pp. 2375-2385.
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Abstract
Given a target image as query, person re-identification systems retrieve a ranked list of candidate matches on a per-camera basis. In deployed systems, a human operator scans these lists and labels sighted targets by touch or mouse-based selection. However, classical re-id approaches generate per-camera lists independently. Therefore, target identifications by operator in a subset of cameras cannot be utilized to improve ranking of the target in remaining set of network cameras. To address this shortcoming, we propose a novel sequential multi-camera re-id approach. The proposed approach can accommodate human operator inputs and provides early gains via a monotonic improvement in target ranking. At the heart of our approach is a fusion function which Given a target image as query, person re-identification systems retrieve a ranked list of candidate matches on a per-camera basis. In deployed systems, a human operator scans these lists and labels sighted targets by touch or mouse-based selection. However, classical re-id approaches generate per-camera lists independently. Therefore, target identifications by operator in a subset of cameras cannot be utilized to improve ranking of the target in remaining set of network cameras. To address this shortcoming, we propose a novel sequential multi-camera re-id approach. The proposed approach can accommodate human operator inputs and provides early gains via a monotonic improvement in target ranking. At the heart of our approach is a fusion function which operates on deep feature representations of query and candidate matches. We formulate an optimization procedure custom-designed to incrementally improve query representation. Since existing evaluation methods cannot be directly adopted to our setting, we also propose two novel evaluation protocols. The results on two large-scale re-id datasets (Market-1501, DukeMTMC-reID) demonstrate that our multi-camera method significantly outperforms baselines and other popular feature fusion schemes. Additionally, we conduct a comparative subject-based study of human operator performance. The superior operator performance enabled by our approach makes a compelling case for its integration into deployable video-surveillance systems. © 2005-2012 IEEE.
| Item Type: | Journal Article |
|---|---|
| Publication: | IEEE Transactions on Information Forensics and Security |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Cameras; Large dataset; Measurement; Monitoring; Network protocols; Optimization; Personnel training; Robustness (control systems); Search engines; Space surveillance, Feature fusion; Feature representation; Operator-in-the-loop; Optimization procedures; Person re identifications; Query representations; Target identification; Video surveillance systems, Security systems |
| Department/Centre: | Division of Interdisciplinary Sciences > Computational and Data Sciences |
| Date Deposited: | 24 Jan 2023 04:59 |
| Last Modified: | 24 Jan 2023 04:59 |
| URI: | https://eprints.iisc.ac.in/id/eprint/79316 |
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