Robust Multi-camera People Tracking Using Maximum Likelihood Estimation

Autor:	Dirk Van Haerenborgh, Peter Veelaert, Jorge Oswaldo Niño-Castañeda, Wilfried Philips, Nyan Bo Bo, Peter Van Hese, Dimitri Van Cauwelaert, Sebastian Gruenwedel, Junzhi Guan
Rok vydání:	2013
Předmět:	Task (computing) Computer science business.industry Tracking system Computer vision Artificial intelligence Smart camera Object (computer science) Tracking (particle physics) business Sensor fusion
Zdroj:	Advanced Concepts for Intelligent Vision Systems ISBN: 9783319028941 ACIVS
DOI:	10.1007/978-3-319-02895-8_53
Popis:	This paper presents a new method to track multiple persons reliably using a network of smart cameras. The task of tracking multiple persons is very challenging due to targets' non-rigid nature, occlusions and environmental changes. Our proposed method estimates the positions of persons in each smart camera using a maximum likelihood estimation and all estimates are merged in a fusion center to generate the final estimates. The performance of our proposed method is evaluated on indoor video sequences in which persons are often occluded by other persons and/or furniture. The results show that our method performs well with the total average tracking error as low as 10.2 cm. We also compared performance of our system to a state-of-the-art tracking system and find that our method outperforms in terms of both total average tracking error and total number of object loss.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::00ad8170561b3e6201f1dd76f7cba60d https://doi.org/10.1007/978-3-319-02895-8_53 Zobrazit plný text záznamu