Multi-Depth Deep Similarity Learning for Person Re-Identification

Abstract

Detecting same people in different surveillance cameras, named person re-identification, has become a challenging and critical task in image processing. Since surveillance images usually have low resolution and different viewpoints, matching persons on them is still difficult. In this paper, a proposed method for person re-identification is introduced based on exploring similarity in different depth layers of convolutional neural network (CNN). To this end, after determining each person as a category for training CNN, optimum filters are obtained to find the best discriminative feature maps based on them. Smoothed discriminative features (SDF) are defined to compute similarity between persons. Experimental results, performed on CUHK01 database, demonstrate that the proposed method outperforms state-of-the-art feature extraction methods for person re-identification.

A New Model for Person Reidentification Using Deep CNN and Autoencoders

“…In another method recently proposed for recognizing a person, researchers have presented a model that uses the characteristics of a person's ranking in the ranking list of the gallery [10]. By computing similarity from different layers of deep convolutional neural networks, a model is proposed by Sezavar et al [11] to find the best filter in each layer that better describes the similarities. Although other methods were introduced before to use these features, they are based on data post-processing, and in the training phase, the network does not have access to the features of the ranking list.…”

Integration of the latent variable knowledge into deep image captioning with Bayesian modeling

“…It is not straightforward to describe and learn this volume of extensive image data, and sophisticated modeling techniques are needed. Today, deep learning techniques have made significant advances in computer vision and NLP due to hardware advancements and the availability of massive amounts of data [2,3]. Deep convolutional neural networks (CNNs) have recently become a key component of many image description systems.…”