ROBUST OBJECT RE-IDENTIFICATION WITH GRASSMANN SUBSPACE FEATURE FOR KEY POINTS AGGREGATION

摘要

Object Re-identification is a key technology for enabling mobile visual search, virtual reality and augmented reality, and a variety of security and surveillance applications. One key problem in re-identification is to have effective key point feature aggregation schemes that can preserve recall performance in short listing, while offering indexing and hashing efficiency. In the MPEG Compact Descriptor for Visual Search (CDVS) Standardization effort, the Scalable Compressed Fisher Vector (SCFV) was proved to be most efficient in this role. In this paper we develop a novel aggregation scheme that captures the subspaces where key points reside, and uses the Grassmannian distance metric to discriminate the aggregated information. Simulation demonstrates that the proposed scheme captures discriminative information complementary to the Fisher Vector (FV) aggregation, and can significantly improve the matching performance.