Comparative Study of the Behavior of Feature Reduction Methods in Person Re-identification Task Bahram Lavi 1 , Mehdi Fatan Serj 2 and Domenec Puig Valls 2 1 Dept. of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy 2 Dept. of Computer Engineering and Maths School of Engineering, Universitat Rovira i Virgili, Tarragona, Spain Keywords: Person Re-Identification, Video Surveillance Systems, Dimensional Reduction Methods. Abstract: One of the goals of person re-identification systems is to support video-surveillance operators and forensic investigators to find an individual of interest in videos acquired by a network of non-overlapping cameras. This is attained by sorting images of previously observed individuals for decreasing values of their similarity with a given probe individual. Existing appearance descriptors, together with their similarity measures, are mostly aimed at improving ranking quality. Many of these descriptors generate a high feature vector represented as an image signature. To tackle person re-identification in real-world scenario the processing time will be crucial, so an individual of interest within a network camera should be found out swiftly. We therefore study some feature reduction methods to achieve a significant trade-off between processing time and ranking quality. Although, observing some redundancies on the generated patterns of a given descriptor are not deniable, we suggest to employ a feature reduction method before use of it in real-world scenarios. In particular, we have tested three reduction methods: PCA, KPCA, and Isomap. We then evaluate our study on two benchmark data sets (VIPeR, and i-LIDS), by using two state-of-the-art descriptors on person re-identification task. The results presented in this paper, after applying the feature reduction step, are very promising in terms of recognition rate. 1 INTRODUCTION Person re-identification is a computer vision task con- sisting of recognizing an individual who had previ- ously been observed over a network of cameras with non-overlapping fields of view (Bedagkar-Gala and Shah, 2014). One of its applications consists of sup- porting video surveillance operators and forensic in- vestigators in retrieving all the videos showing an in- dividual of interest, given an image of him/her as a query (aka probe). In this application scenario, the goal of a person re-identification system is returning to the user the frames or videos of all the individuals recorded by the camera network (aka template gal- lery) sorted for decreasing similarity to the probe, so that the user can find the occurrences (if any) of the in- dividual of interest, ideally in the first positions. This task is challenging due to several issues typical of vi- deo surveillance footage, like low resolution, uncon- strained pose, illumination changes, and occlusions, which do not allow to exploit strong biometrics like face. Clothing appearance is therefore one of the most widely used cues. Other cues like gait and anthropo- metric measures have also been investigated. Many of the existing similarity measures (either hand-crafted or learnt from data) are indeed rather complex, and require a relatively high processing time, e.g., (Farenzena et al., 2010; Ma et al., 2014; Liao et al., 2015). On the other hand, in real-world ap- plications the template gallery can be very large, and even if the processing time for a single matching score is low (e.g., the Euclidean distance between fixed- length feature vectors (Ma et al., 2014)), evaluating the matching scores for all the templates can be time- consuming. During the past few years, many descriptors have been proposed in the literature based on clothing ap- pearance. The existing descriptors are typically con- structed based on either colour information, texture information, or combination of both. Despite the dif- ferences among them, the final descriptor is typically generated in high-dimension feature-size. In some cases, the operator intends to attain the result much faster, because of existing too many templates to be checked by him/her. We clearly disccused this issue in (Lavi et al., 2016) by proposing multi-stage sy- 614 Lavi, B., Serj, M. and Valls, D. Comparative Study of the Behavior of Feature Reduction Methods in Person Re-identification Task. DOI: 10.5220/0006717906140621 In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2018), pages 614-621 ISBN: 978-989-758-276-9 Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved