People Tracking and Re-Identifying in Distributed Contexts: Extension Study of PoseTReID

• Main Authors: Siv, Ratha, Mancas, Matei, Gosselin, Bernard, Valy, Dona, Sreng, Sokchenda
• Format: Journal Article
• Language: English
• Published: 20-05-2022
• Subjects: Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.2205.10086

In our previous paper, we introduced PoseTReID which is a generic framework
for real-time 2D multi-person tracking in distributed interaction spaces where
long-term people's identities are important for other studies such as behavior
analysis, etc. In this paper, we introduce a further study of PoseTReID
framework in order to give a more complete comprehension of the framework. We
use a well-known bounding box detector YOLO (v4) for the detection to compare
to OpenPose which was used in our last paper, and we use SORT and DeepSORT to
compare to centroid which was also used previously, and most importantly for
the re-identification, we use a bunch of deep leaning methods such as MLFN,
OSNet, and OSNet-AIN with our custom classification layer to compare to FaceNet
which was also used earlier in our last paper. By evaluating on our PoseTReID
datasets, even though those deep learning re-identification methods are
designed for only short-term re-identification across multiple cameras or
videos, it is worth showing that they give impressive results which boost the
overall tracking performance of PoseTReID framework regardless the type of
tracking method. At the same time, we also introduce our research-friendly and
open source Python toolbox pyppbox, which is purely written in Python and
contains all sub-modules which are used in this study along with real-time
online and offline evaluations for our PoseTReID datasets. This pyppbox is
available on GitHub https://github.com/rathaumons/pyppbox .