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      Weakly Supervised Tracklet Person Re-Identification by Deep Feature-wise Mutual Learning

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          Abstract

          The scalability problem caused by the difficulty in annotating Person Re-identification(Re-ID) datasets has become a crucial bottleneck in the development of Re-ID.To address this problem, many unsupervised Re-ID methods have recently been proposed.Nevertheless, most of these models require transfer from another auxiliary fully supervised dataset, which is still expensive to obtain.In this work, we propose a Re-ID model based on Weakly Supervised Tracklets(WST) data from various camera views, which can be inexpensively acquired by combining the fragmented tracklets of the same person in the same camera view over a period of time.We formulate our weakly supervised tracklets Re-ID model by a novel method, named deep feature-wise mutual learning(DFML), which consists of Mutual Learning on Feature Extractors (MLFE) and Mutual Learning on Feature Classifiers (MLFC).We propose MLFE by leveraging two feature extractors to learn from each other to extract more robust and discriminative features.On the other hand, we propose MLFC by adapting discriminative features from various camera views to each classifier. Extensive experiments demonstrate the superiority of our proposed DFML over the state-of-the-art unsupervised models and even some supervised models on three Re-ID benchmark datasets.

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          Most cited references20

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          Scalable Person Re-identification: A Benchmark

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            DeepReID: Deep Filter Pairing Neural Network for Person Re-identification

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              Performance Measures and a Data Set for Multi-target, Multi-camera Tracking

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                Author and article information

                Journal
                31 October 2019
                Article
                1910.14333
                59af9d7c-7d72-4073-b8e9-2b0775146371

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                cs.CV

                Computer vision & Pattern recognition
                Computer vision & Pattern recognition

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