An Improved Person Re-identification Method by light-weight convolutional neural network

• URL: http://arxiv.org/abs/2008.09448v1
• Date: Fri, 21 Aug 2020 12:34:15 GMT
• Title: An Improved Person Re-identification Method by light-weight convolutional neural network
• Authors: Sajad Amouei Sheshkal, Kazim Fouladi-Ghaleh, Hossein Aghababa
• Abstract summary: Person Re-identification is faced with challenges such as low resolution, varying poses, illumination, background clutter, and occlusion. The present paper aims to improve Person Re-identification using transfer learning and application of verification loss function. Experiments showed that the proposed model performs better than state-of-the-art methods on the CUHK01 dataset.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Person Re-identification is defined as a recognizing process where the person is observed by non-overlapping cameras at different places. In the last decade, the rise in the applications and importance of Person Re-identification for surveillance systems popularized this subject in different areas of computer vision. Person Re-identification is faced with challenges such as low resolution, varying poses, illumination, background clutter, and occlusion, which could affect the result of recognizing process. The present paper aims to improve Person Re-identification using transfer learning and application of verification loss function within the framework of Siamese network. The Siamese network receives image pairs as inputs and extract their features via a pre-trained model. EfficientNet was employed to obtain discriminative features and reduce the demands for data. The advantages of verification loss were used in the network learning. Experiments showed that the proposed model performs better than state-of-the-art methods on the CUHK01 dataset. For example, rank5 accuracies are 95.2% (+5.7) for the CUHK01 datasets. It also achieved an acceptable percentage in Rank 1. Because of the small size of the pre-trained model parameters, learning speeds up and there will be a need for less hardware and data.

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