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Unbiased Spatio-Temporal Representation with Uncertainty Control for Person Re-identification

—For person re-identiﬁcation, most current research aims to encode the spatial and temporal information by using convolutional neural networks (CNNs) to extract spatial features and recurrent neural networks (RNNs) or their variations to discover the time dependencies. However, it ignores the effect of the complex background, which leads to a biased spatial representation. Further, it often uses the backpropagation through time (BPTT) to train RNNs. Unfortunately, it is hard to learn the long-term dependency via BPTT due to the gradient vanishing or exploding. The signiﬁcance of a frame should not be biased by its position in a given sequence. In this paper, a new method is proposed to learn an unbiased semantic representation for video-based person re-identiﬁcation. To handle the background clutter and occlusion, a two-branch CNN model is used to obtain the enriched representation from both the foreground person and original pedestrian images. Then, an unbiased bidirectional convolutional neural network architecture is developed to learn the unbiased spatial and temporal representation. Experimental results on three public datasets demonstrate the effectiveness of the proposed method.