Abstract
In this paper, we propose a novel loss function named the hybrid quadruplet loss (HQL) to utilize the generated samples for pedestrian retrieval in sensor networks. The proposed HQL employs a set of quadruplets in order to maintain an appropriate margin between the real sample and the generated sample, reduce the intra-class variations and enlarge the inter-class variations. By this way, the generalization of the deep model could be improved. Furthermore, to identify the extremely similar pedestrians, we propose a novel multistream layer to mine imperceptible information from different aspects. The proposed multistream layer utilizes various filters with different morphologies to capture discriminative features in multiple scales, and it is flexible to follow any convolutional layer. Experiments on the three large-scale pedestrian retrieval databases (Market1501, CUHK03, and DukeMTMC-reID) demonstrate that the proposed method outperforms other state-of-the-art methods.
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