A Bayesian Approach on People Localization in Multicamera Systems

Utasi, Á and Benedek, Csaba (2013) A Bayesian Approach on People Localization in Multicamera Systems. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 23 (1). pp. 105-115. ISSN 1051-8215 MTMT:1923056; doi:10.1109/TCSVT.2012.2203201


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In this paper we introduce a Bayesian approach on multiple people localization in multi-camera systems. First, pixel-level features are extracted, which are based on physical properties of the 2-D image formation process, and provide information about the head and leg positions of the pedestrians, distinguishing standing and walking people, respectively. Then features from the multiple camera views are fused to create evidence for the location and height of people in the ground plane. This evidence accurately estimates the leg position even if either the area of interest is only a part of the scene, or the overlap ratio of the silhouettes from irrelevant outside motions with the monitored area is significant. Using this information we create a 3-D object configuration model in the real world. We also utilize a prior geometrical constraint, which describes the possible interactions between two pedestrians. To approximate the position of the people, we use a population of 3-D cylinder objects, which is realized by a Marked Point Process. The final configuration results are obtained by an iterative stochastic energy optimization algorithm. The proposed approach is evaluated on two publicly available datasets, and compared to a recent state-of-the-art technique. To obtain relevant quantitative test results, a 3-D Ground Truth annotation of the real pedestrian locations is prepared, while two different error metrics and various parameter settings are proposed and evaluated, showing the advantages of our proposed model.

Item Type: ISI Article
Subjects: Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions: Distributed Events Analysis Research Laboratory
SWORD Depositor: MTMT Injector
Depositing User: EPrints Admin
Date Deposited: 05 Feb 2014 12:33
Last Modified: 11 Apr 2014 17:52
URI: https://eprints.sztaki.hu/id/eprint/7505

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