A multi-configuration part-based person detector

Abstract

Proceedings of the Special Session on Multimodal Security and Surveillance Analytics 2014, held during the International Conference on Signal Processing and Multimedia Applications (SIGMAP 2014) in ViennaPeople detection is a task that has generated a great interest in the computer vision and specially in the surveillance community. One of the main problems of this task in crowded scenarios is the high number of occlusions deriving from persons appearing in groups. In this paper, we address this problem by combining individual body part detectors in a statistical driven way in order to be able to detect persons even in case of failure of any detection of the body parts, i.e., we propose a generic scheme to deal with partial occlusions. We demonstrate the validity of our approach and compare it with other state of the art approaches on several public datasets. In our experiments we consider sequences with different complexities in terms of occupation and therefore with different number of people present in the scene, in order to highlight the benefits and difficulties of the approaches considered for evaluation. The results show that our approach improves the results provided by state of the art approaches specially in the case of crowded scenesThis work has been done while visiting the Communication Systems Group at the Technische Universität Berlin (Germany) under the supervision of Prof. Dr.-Ing. Thomas Sikora. This work has been partially supported by the Universidad Aut´onoma de Madrid (“Programa propio de ayudas para estancias breves en España y extranjero para Personal Docente e Investigador en Formación de la UAM”), by the Spanish Government (TEC2011-25995 EventVideo) and by the European Community’s FP7 under grant agreement number 261776 (MOSAIC)