Robust Anchor Embedding for Unsupervised
Video Person Re-Identification in the Wild
Abstract. This paper addresses the scalability and robustness issues
of estimating labels from imbalanced unlabeled data for unsupervised
video-based person re-identification (re-ID). To achieve it, we propose a
novel Robust AnChor Embedding (RACE) framework via deep feature
representation learning for large-scale unsupervised video re-ID. Within
this framework, anchor sequences representing different persons are firstly selected to formulate an anchor graph which also initializes the CNN
model to get discriminative feature representations for later label estimation. To accurately estimate labels from unlabeled sequences with noisy
frames, robust anchor embedding is introduced based on the regularized
affine hull. Efficiency is ensured with kNN anchors embedding instead of
the whole anchor set under manifold assumptions. After that, a robust
and efficient top-k counts label prediction strategy is proposed to predict the labels of unlabeled image sequences. With the newly estimated
labeled sequences, the unified anchor embedding framework enables the
feature learning process to be further facilitated. Extensive experimental results on the large-scale dataset show that the proposed method
outperforms existing unsupervised video re-ID methods