As social robots increasingly engage with humans in dynamic environments, one of the main challenges is to develop a perception system that can detect and track people’s position, velocity and orientation in real time. By integrating RGBD data with multi-object tracking frameworks, this work seeks to provide a reliable solution for 4D pose estimation. The tracking-by-detection paradigm, which separates the detection phase from the tracking phase, has established itself as one of the most used approaches for online, real-time multi-object tracking applications. Following this paradigm, three methods were studied, developed and tested, in a progressive approach to improve the tracking accuracy and the quality of the estimated 4D poses.