209 research outputs found
SVDNet for Pedestrian Retrieval
© 2017 IEEE. This paper proposes the SVDNet for retrieval problems, with focus on the application of person re-identification (reID). We view each weight vector within a fully connected (FC) layer in a convolutional neuron network (CNN) as a projection basis. It is observed that the weight vectors are usually highly correlated. This problem leads to correlations among entries of the FC descriptor, and compromises the retrieval performance based on the Euclidean distance. To address the problem, this paper proposes to optimize the deep representation learning process with Singular Vector Decomposition (SVD). Specifically, with the restraint and relaxation iteration (RRI) training scheme, we are able to iteratively integrate the orthogonality constraint in CNN training, yielding the so-called SVDNet. We conduct experiments on the Market-1501, CUHK03, and DukeMTMC-reID datasets, and show that RRI effectively reduces the correlation among the projection vectors, produces more discriminative FC descriptors, and significantly improves the re-ID accuracy. On the Market-1501 dataset, for instance, rank-1 accuracy is improved from 55.3% to 80.5% for CaffeNet, and from 73.8% to 82.3% for ResNet-50
Improved Res2Net model for Person re-identification
Person re-identification has become a very popular research topic in the
computer vision community owing to its numerous applications and growing
importance in visual surveillance. Person re-identification remains challenging
due to occlusion, illumination and significant intra-class variations across
different cameras. In this paper, we propose a multi-task network base on an
improved Res2Net model that simultaneously computes the identification loss and
verification loss of two pedestrian images. Given a pair of pedestrian images,
the system predicts the identities of the two input images and whether they
belong to the same identity. In order to obtain deeper feature information of
pedestrians, we propose to use the latest Res2Net model for feature extraction
of each input image. Experiments on several large-scale person
re-identification benchmark datasets demonstrate the accuracy of our approach.
For example, rank-1 accuracies are 83.18% (+1.38) and 93.14% (+0.84) for the
DukeMTMC and Market-1501 datasets, respectively. The proposed method shows
encouraging improvements compared with state-of-the-art methods.Comment: 6 page
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