Using latent features for short-term person re-identification with RGB-D cameras. Issue 2 (May 2016) - Oliver, Javier; Albiol, Alberto; Albiol, Antonio; Mossi, José
Abstract This paper presents a system for people re-identification in uncontrolled scenarios using RGB-depth cameras. Compared to conventional RGB cameras, the use of depth information greatly simplifies the tasks of segmentation and tracking. In a previous work, we proposed a similar architecture where people were characterized using color-based descriptors that we named bodyprints. In this work, we propose the use oflatent feature models to extract more relevant information from the bodyprint descriptors by reducing their dimensionality.Latent features can also cope with missing data in case of occlusions. Different probabilisticlatent feature models, such as probabilistic principal component analysis and factor analysis, are compared in the paper. The main difference between the models is how the observation noise is handled in each case. Re-identification experiments have been conducted in a real store where people behaved naturally. The results show that the use of thelatent features significantly improves the re-identification rates compared to state-of-the-art works.