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 multiframe tracking (MFT) and the improved MFT (IMFT) is proposed for enhancing computational efficiency in the longterm 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.