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

TL;DR

This paper extends the PoseTReID framework for multi-person tracking by evaluating various detection and re-identification methods, demonstrating improved performance and providing an open-source Python toolbox for research use.

Contribution

It introduces a comprehensive evaluation of different detection and deep learning re-identification methods within PoseTReID and releases an open-source toolbox for the community.

Findings

Deep learning re-identification methods improve tracking performance.

PoseTReID performs well across different detection and re-identification combinations.

Open-source toolbox facilitates research and evaluation.

Abstract

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 .