JUCS - Journal of Universal Computer Science 25(6): 611-626, doi: 10.3217/jucs-025-06-0611
Improving Person Re-identification by Segmentation-Based Detection Bounding Box Filtering
expand article infoDominik Pieczyński‡, Marek Kraft‡, Michał Fularz‡
‡ Poznań University of Technology, Poznań, Poland

Corresponding author: Dominik Pieczyński ( dominik.pieczynski@put.poznan.pl )

© Dominik Pieczyński, Marek Kraft, Michał Fularz.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY-ND 4.0). This license allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use.
Citation: Pieczyński D, Kraft M, Fularz M (2019) Improving Person Re-identification by Segmentation-Based Detection Bounding Box Filtering. JUCS - Journal of Universal Computer Science 25(6): 611-626. https://doi.org/10.3217/jucs-025-06-0611
Open Access
Abstract
In this paper, a method for improving the quality of person re-identification results is presented. The method is based on the assumption, that including segmentation information into re-identi_cation pipeline discards the automated detections that are of poor quality due to occlusions, misplaced regions of interest (ROI), multiple persons found within a single ROI, etc. using a simple segment number, bounding box fill rate and aspect ratio check. Assuming that a joint detector-segmented approach is used, the additional cost associated with the use of the proposed approach is very low.
Keywords
person re-identification, computer vision, deep learning, segmentation