Comparing Trackers for Multiple Targets in Warehouses

Abstract

Multiple object tracking is a complex and challenging computer vision problem. In industrial premises like warehouses, a robust multiple object tracking framework could provide useful information on lift truck and pedestrian movements. This information could be utilized for process improvement. It could also potentially promote greater safety awareness in the facility. We evaluate and analyze selected model-based and deep feature-based tracking mechanisms suitable for warehouse environments. Objects (or targets) are forklift trucks or people. Our experimental comparison and discussion facilitates useful insights into the design of robust multiple target trackers in warehouses, their limitations, and future research directions.