Multiple people tracking from 2D depth data by deterministic spatiotemporal data association

This paper proposes a deterministic approach to track people in a populated environment from 2D depth data by a laser range finder attached on a mobile robot. This work aims to improve robustness of multiple people tracking in the presence of change of the number of people, missing data, and long-term occlusions by using spatiotemporal data association. The temporal data association method is based on the multi-frame tracking (MFT) and the improved MFT (IMFT) is proposed for enhancing computational efficiency in the long-term occlusions. A spatial data association algorithm used a matching algorithm from the leg history data for detecting a human subject from leg tracks. The proposed methodology has been assessed in the three walking patterns of two people and compared with MFT and MHT methods.