Paper Info
Title
Remote feature learning for mobile re-identification
Abstract
This work introduces a novel method for person re-identification using embedded smart cameras. State-of-the-art methods address the re-identification problem using global and local features, metric learning and feature transformation algorithms. Such methods require advanced systems with high computational capabilities. Nowadays, there is a growing interest in security applications using embedded cameras. Motivated by this we propose to study a new system that addresses the challenges posed by the reidentification problem using devices (e.g. smartphones, etc.) that have limited resources. In this work we introduce a novel client-server system that exploits a feature learning method to achieve a two-fold objective: (i) maximize the re-identification performance over time and (ii) reduce the required computational costs. In the training phase, state-of-the-art features are selected considering both the device capabilities and re-identification performance. During the detection phase, the re-identification performance are maximized by selecting the best features for a given input image. To demonstrate the performance of the proposed method we conduct the experiments using different mobile devices. Statistics about feature extraction and feature matching are presented together with re-identification results.
Year
DOI
Venue
2013
10.1109/ICDSC.2013.6778221
2013 Seventh International Conference on Distributed Smart Cameras (ICDSC)
Keywords
DocType
Citations 
smart phone,feature matching,feature extraction,statistical analysis,client-server system,security application,feature transformation algorithm,metric learning,local feature,global feature,embedded smart camera,person reidentification,mobile reidentification,remote feature learning
Conference
3
PageRank 
References 
Authors
0.39
10
4
Name
Order
Citations
PageRank
Marco Vernier151.79
Niki Martinel234924.39
C. Micheloni393462.52
Gian Luca Foresti478183.01