Affiliations: [a] Intelligent Systems Lab Research Group, Universidad Carlos III de Madrid, Leganés, Madrid, Spain | [b] Multimedia and Vision Research Group, Queen Mary University of London, London, UK
Correspondence:
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Corresponding author: María José Gómez-Silva, Intelligent Systems Lab (LSI) Research Group, Universidad Carlos III de Madrid, Avda. de la Universidad, 30, 28911 Leganés, Madrid, Spain. E-mail: [email protected].
Abstract: Learning to discriminate, whether two person-images correspond to the same person or not, is a daunting challenge when only two images per person are available. This task is called single-shot person re-identification (re-id) and it assumes that each one of the two available images was captured from a different camera view entailing variations in pose, resolution, scale, illumination and background. Addressing this task through supervised training of a deep convolutional neural network is susceptible to model overfitting due to the critical lack of enough labelled data. This paper proposes to exploit the transference of learning previously acquired from a multi-object-tracking (MOT) domain. In this context, a unique deep triplet architecture has been trained on both domains. Six different levels of transfer learning have been implemented and evaluated, proving that the transference of leaning from a different domain remarkably increases the re-id performance. Experimental results validate accuracy and robustness of the proposed method as comparable to other state-of-the-art techniques. These results also confirm that, despite the data problem, deep learning is also applicable to the single-shot re-id task.
Keywords: Transfer learning, deep learning, person re-identification, multi-object tracking, pair-wise binary classification
