Despite much recent progress in video-based person re-identification (re-ID),
the current state-of-the-art still suffers from common real-world challenges
such as appearance similarity among various people, occlusions, and frame
misalignment. To alleviate these problems, we propose Spatio-Temporal
Representation Factorization (STRF), a flexible new computational unit that can
be used in conjunction with most existing 3D convolutional neural network
architectures for re-ID. The key innovations of STRF over prior work include
explicit pathways for learning discriminative temporal and spatial features,
with each component further factorized to capture complementary person-specific
appearance and motion information. Specifically, temporal factorization
comprises two branches, one each for static features (e.g., the color of
clothes) that do not change much over time, and dynamic features (e.g., walking
patterns) that change over time. Further, spatial factorization also comprises
two branches to learn both global (coarse segments) as well as local (finer
segments) appearance features, with the local features particularly useful in
cases of occlusion or spatial misalignment. These two factorization operations
taken together result in a modular architecture for our parameter-wise light
STRF unit that can be plugged in between any two 3D convolutional layers,
resulting in an end-to-end learning framework. We empirically show that STRF
improves performance of various existing baseline architectures while
demonstrating new state-of-the-art results using standard person re-ID
evaluation protocols on three benchmarks.

该研究提出了一个新的 Spatio-Temporal Representation Factorization (STRF) 计算单元，它可以与大多数现有的三维卷积神经网络结构一起使用。结果表明，STRF 在三个基准测试中展现出了比现有基线结构更好的表现。