The ability to identify the specific person in a home camera network is very relevant for healthcare applications where humans need to be observed daily in their living environment. The appearance based people identification in a domestic environment has many similarities with the problem of re-identification in public surveillance systems, but there are also some additional beneficial and constraining factors (e.g., less people, non-pedestrian behaviour, unusual camera viewpoints). In this paper, we are considering the problem of people identification in a small home RGB-D camera network, for which we have developed a method based on appearance learning and classification using a combination of SVM and the Naive Bayes classifier. The method is evaluated using the prototype of a real-time multiple camera system on a 16 people dataset.
|Title of host publication||Proceedings of 11th International Multi-Conference on Systems, Signals & Devices - SSD 2014, 11-14 February 2014, Barcelona, Spain|
|Place of Publication||Piscataway|
|Publisher||Institute of Electrical and Electronics Engineers|
|Publication status||Published - 2015|