Person re-identification using prioritized chromatic texture (PCT) with deep learning
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Person re-identification using prioritized chromatic texture (PCT) with deep learning K. Jayapriya 1 & I. Jeena Jacob 2 & N. Ani Brown Mary 3 Received: 24 March 2020 / Revised: 30 July 2020 / Accepted: 4 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Person re-identification (re-ID) helps to identify a person’s attention in different cameras. But this is not an easy task, due to distance, illumination and lack of dataset. Nowadays, this field attracts many researchers because of its varied applications. Here, the information of both local texture and global color representations are concatenated with an original raw image. This concatenated information is gathered by finding the maximum value of chrominance in terms of HSV, texture in terms of Scale Invariant Local Ternary Pattern (SILTP) for each pixel and original raw image. SILTP is well known for its illumination invariant texture description. Convolutional Neural Network (CNN) is used in the proposed work to extract the features from the concatenated information. The proposed Prioritized Chromatic Texture Image (PCTimg) is concatenated with original raw image and fed into CNN. Here, finally a six dimensionalfeature is fed into CNN to extract the deep features. Cross-view Quadratic Discriminant Analysis (XQDA) similarity metric algorithm is employed to re-identify a person.Multiscale Retinex algorithm is used for pre-processing the images.To address the challenges in terms of view point deflection, a sliding window is formed for describing local details of a person in the SILTP feature extraction phase. The HSV helps to incorporate the human color perception. The triplet loss function is used to learn the similarity and the dissimilarity of the training images. The performance analysis of the proposed work is improvedwhen compared to the existingworks. Keywords Person re-identification . Deep learning . Convolutional neural network . XQDA
* K. Jayapriya [email protected] I. Jeena Jacob [email protected] N. Ani Brown Mary [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications
1 Introduction Nowadays, a variety of tasks are performed using the intelligent videos, the multimedia data and the mobile terminals. Person re-identification is used to find the similarity of a person in different non-overlapping camera views over distributed open spaces [5].The increasing demand of person re-identification is improved due to the public safety and other applications in robotics, multimedia and forensics attributes [21]. The variation in person re-identification is an inherently challenging task because person visual appearance may change dramatically in different camera illumination, occlusion, background, scale, viewing angle, pose, etc. This makes the task more challengeable. Person re-identification involves person detection, person tracking, and person retrieval [21]. Most of the individual images are snapped by disjoint and non-overlapping
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