title: Cluster-based distributed face tracking in camera networks.
abstract: In this paper, we present a distributed multicamera face tracking system suitable for large wired camera networks. Unlike previous multicamera face tracking systems, our system does not require a central server to coordinate the entire tracking effort. Instead, an efficient camera clustering protocol is used to dynamically form groups of cameras for in-network tracking of individual faces. The clustering protocol includes cluster propagation mechanisms that allow the computational load of face tracking to be transferred to different cameras as the target objects move. Furthermore, the dynamic election of cluster leaders provides robustness against system failures. Our experimental results show that our cluster-based distributed face tracker is capable of accurately tracking multiple faces in real-time. The overall performance of the distributed system is comparable to that of a centralized face tracker, while presenting the advantages of scalability and robustness.
authors.affiliation: School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA. yoder2@purdue.edu
authors.forename: Josiah
authors.initials: J
authors.lastname: Yoder
authors.forename: Henry
authors.initials: H
authors.lastname: Medeiros
authors.forename: Johnny
authors.initials: J
authors.lastname: Park
authors.forename: Avinash C
authors.initials: AC
authors.lastname: Kak
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000379:methods*; D016000:Cluster Analysis; D003195:Computer Communication Networks; D019416:Head Movements / Q000502:physiology*; D006801:Humans; D009633:Nonverbal Communication / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000295:instrumentation* / Q000379:methods*; D011159:Population Surveillance / Q000379:methods; D011187:Posture / Q000502:physiology*