Abstract
Person reidentification (ReID) is a challenging task of finding a target pedestrian in a gallery set collected from multiple nonoverlapping camera views. Recently, state-of-the-art ReID performance has been achieved via an end-to-end trainable deep neural network framework, which integrates convolution feature extraction, similarity learning and reranking into a joint optimization framework. In such a framework, the similarity is learned via an embedding network, the reranking is conducted with a random walk, and the whole framework is optimized with a cross-entropy-based verification loss. Unfortunately, the embedding net is difficult to train well because their two-dimensional outputs mutually interfere each other when using the conventional random walk. In addition, the supervision information has not been fully exploited during the training phase due to the binary nature of the verification loss. In this paper, we propose a novel approach, called group-shuffling dual random walks with label smoothing (GSDRWLS), in which random walks are performed separately on two channels—one for positive verification and one for negative verification—and the binary verification labels are properly modified with an adaptive label smoothing technique before feeding into the verification loss in order to train the overall network effectively and to avoid the overfitting problem. Extensive experiments conducted on three large benchmark datasets, including CUHK03, Market-1501 and DukeMTMC, confirm the superior performance of our proposal.
Highlights
Person reidentification (ReID) aims to match pedestrain across multiple cameras and has increasingly gained attetion in computer vision and pattern recognition community, due to its importance to video surveillance analysis
We propose to address the shortcomings in the integrated end-to-end framework [17] by three ingredients: a) conducting dual random walks in two separate channels, one for positive verification information and one for negative verification information; b) using an adaptive labelsmoothing technique in the cross-entropy-based verification loss, to effectively exploit both the positive verification labels and the negative verification labels; and c) implementing a random walk in the training phase without splitting each minibatch data into a probe set and a gallery set in order to obtain more supervision information
We propose an adaptive label-smoothing technique for the verification loss to address the overfitting problem, in which the smoothing term is associated with the number of identities
Summary
Person reidentification (ReID) aims to match pedestrain across multiple cameras and has increasingly gained attetion in computer vision and pattern recognition community, due to its importance to video surveillance analysis. Person ReID is quit challenging because of the heavy variations from different viewpoints, varying illumination, changing weather conditions, clutter background and etc. Traditional approaches addressing the person ReID task are roughly from three perspectives: a) feature extraction, b) metric learning, and c) reranking. In [1]–[4], hand-crafted features based on color, texture or their combination are designed. It is a challenge for these methods to capture enough discriminative information. In [5]–[10], metric learning-based methods are proposed to learn a
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