Visual Cognition–Inspired Multi-View Vehicle Re-Identification via Laplacian-Regularized Correlative Sparse Ranking
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Vehicle re-identification has gradually gained attention and widespread applications. However, most of the existing methods learn the discriminative features for identities by single-feature channel only. It is worth noting that visual cognition of the human eyes is a multi-channel system which usually seeks a sparse representation. Therefore, integrating the multi-view information in sparse representation is a natural way to boost computer vision tasks in challenging scenarios. In this paper, we propose to mine multi-view deep features via Laplacian-regularized correlative sparse ranking for vehicle re-identification. Specifically, first, we employ multiple baseline networks to generate features. Then, we explore the feature correlation via enforcing the correlation term into the multi-view Laplacian sparse ranking framework. The original rankings are obtained by the reconstruction coefficients between the probe and gallery. Finally, we utilize a re-ranking technique to further boost performance. Experimental results on public benchmark VeRi-776 and VehicleID datasets demonstrate that our approach outperforms state-of-the-art approaches. The Laplacian-regularized correlative sparse ranking as a general framework can be used in any multi-view feature fusion and will obtain more competitive results.
KeywordsVehicle re-identification Laplacian-regularized correlative sparse ranking Multi-view Deep feature
This research is supported in part by the National Natural Science Foundation of China (61976002, 61602001, 61671018 and 61860206004), the Open Project Program of the National Laboratory of Pattern Recognition (NLPR) (201900046), and the Natural Science Foundation of Anhui Higher Education Institutions of China (KJ2019A0033); Open fund for Discipline Construction, Institute of Physical Science and Information Technology, Anhui University.
Compliance with Ethical Standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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