Journal Paper

Paper Title - Person Re-Identification Using Texture Driven Deep Learning


Abstract
Person re-identification largely involves input patterns or attributes. Recognizing a person across non-overlapping camera views, with different pose, illumination, and camera characteristics. To propose to tackle this problem by training a deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to common appearance variations encountered in the re-identification problem. Specifically, a Siamese-network architecture is used to train a feature extraction network using pairs of similar and dissimilar images. Keywords- person re-identification, deep learning, neural networks, feature embedding


Author - Bontha Kanchana, B.Suneetha, Pamidi Malathi

Citation - Bontha Kanchana   ,   B.Suneetha   ,   Pamidi Malathi   ,   Bontha Kanchana, B.Suneetha, Pamidi Malathi " Person Re-Identification Using Texture Driven Deep Learning " , International Journal of Industrial Electronics and Electrical Engineering , Volume-5,Issue-4  ( Apr, 2017 )

Indexed - Google Scholar


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| Published on 2017-06-23