One-pass person re-identification by sketch online discriminant analysis

Highlights：

• SoDA can efficiently keep the main data variations of all passed samples in a low rank sketch matrix when processing sequential data samples, and estimate the approximate within class variance from the sketch data information.

• The feature dimension reduction is naturally embedded in the proposed SoDA, and no extra online dimension reduction algorithm is required for high-dimensional data.

• Solid theoretical analysis on how the optimal feature transformation learned by SoDA sequentially approximates its offline version that is learned on all observed data samples.

• Extensive experimental results have shown the effectiveness of our SoDA and empirically support our theoretical analysis.

摘要

•SoDA is proposed as an online person re-identification (re-id) approach to effectively alleviate the high cost on space and computational complexity when person re-id methods have to be trained on large-scale or high dimensional data or streaming data.•SoDA can efficiently keep the main data variations of all passed samples in a low rank sketch matrix when processing sequential data samples, and estimate the approximate within class variance from the sketch data information.•The feature dimension reduction is naturally embedded in the proposed SoDA, and no extra online dimension reduction algorithm is required for high-dimensional data.•Solid theoretical analysis on how the optimal feature transformation learned by SoDA sequentially approximates its offline version that is learned on all observed data samples.•Extensive experimental results have shown the effectiveness of our SoDA and empirically support our theoretical analysis.