Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification.

•An improved deep feature embedding approach for person re-identification is presented to learn representations amenable to similarity score computation.•The quality of learned representations and the training efficiency is augmented by jointly optimizing robust feature embedding, local adaptive similarity learning, and suitable positive mining.•An alternative to CNN embedding is presented by formulating a stacked CRBMs into local sample structure in deep feature space, and thus enables local adaptive similarity metric learning as well as plausible positive mining.•Stochastic gradient descent is modified to reuse past computed gradients from neighborhood data points, leading to linear convergence.