Tracking People in Highly Dynamic Industrial Environments

TL;DR

This paper introduces a novel multi-sensor tracking system for dynamic industrial environments, utilizing CCTV, radio, inertial sensors, and cross-modality training to adapt to environmental changes and improve multi-person tracking accuracy.

Contribution

The paper presents a new multi-target tracking framework that leverages cross-modality training and social forces to adapt to environment dynamics in industrial settings.

Findings

Significant accuracy improvements in real-world construction site experiments.

Effective use of occlusion maps for environmental change detection.

Enhanced multi-person tracking in highly dynamic environments.

Abstract

To date, the majority of positioning systems have been designed to operate within environments that have long-term stable macro-structure with potential small-scale dynamics. These assumptions allow the existing positioning systems to produce and utilize stable maps. However, in highly dynamic industrial settings these assumptions are no longer valid and the task of tracking people is more challenging due to the rapid large-scale changes in structure. In this paper we propose a novel positioning system for tracking people in highly dynamic industrial environments, such as construction sites. The proposed system leverages the existing CCTV camera infrastructure found in many industrial settings along with radio and inertial sensors within each worker's mobile phone to accurately track multiple people. This multi-target multi-sensor tracking framework also allows our system to use cross-modality training in order to deal with the environment dynamics. In particular, we show how our system uses cross-modality training in order to automatically keep track environmental changes (i.e. new walls) by utilizing occlusion maps. In addition, we show how these maps can be used in conjunction with social forces to accurately predict human motion and increase the tracking accuracy. We have conducted extensive real-world experiments in a construction site showing significant accuracy improvement via cross-modality training and the use of social forces.