Pfinder: real-time tracking of the human body. Pfinder is a real-time system for tracking people and interpreting their behavior. It runs at 10 Hz on a standard SGI Indy computer, and has performed reliably on thousands of people in many different physical locations. The system uses a multiclass statistical model of color and shape to obtain a 2D representation of head and hands in a wide range of viewing conditions. Pfinder has been successfully used in a wide range of applications including wireless interfaces, video databases, and low-bandwidth coding.

References in zbMATH (referenced in 80 articles )

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  1. Garcia-Garcia, Belmar; Bouwmans, Thierry; Rosales Silva, Alberto Jorge: Background subtraction in real applications: challenges, current models and future directions (2020)
  2. Safaei, Amin; Wu, Q. M. Jonathan; Yang, Yimin: System-on-a-chip (SoC)-based hardware acceleration for foreground and background identification (2018)
  3. Ali, Imtiaz; Mille, Julien; Tougne, Laure: Adding a rigid motion model to foreground detection: application to moving object detection in rivers (2014) ioport
  4. Bouwmans, Thierry: Traditional and recent approaches in background modeling for foreground detection: an overview (2014)
  5. Ji, Zhangjian; Wang, Weiqiang: Detect foreground objects via adaptive fusing model in a hybrid feature space (2014) ioport
  6. Li, Dawei; Xu, Lihong; Goodman, Erik: On-line EM variants for multivariate normal mixture model in background learning and moving foreground detection (2014)
  7. Yeh, Chia-Hung; Lin, Chih-Yang; Muchtar, Kahlil; Kang, Li-Wei: Real-time background modeling based on a multi-level texture description (2014) ioport
  8. Liwicki, Stephan; Tzimiropoulos, Georgios; Zafeiriou, Stefanos; Pantic, Maja: Euler principal component analysis (2013)
  9. Yoon, Changyong; Cheon, Minkyu; Park, Mignon: Object tracking from image sequences using adaptive models in fuzzy particle filter (2013)
  10. Ardö, Håkan; Ågström, Kalle: Bayesian formulation of image patch matching using cross-correlation (2012)
  11. Armanfard, Narges; Komeili, Majid; Kabir, Ehsanollah: TED: A texture-edge descriptor for pedestrian detection in video sequences (2012) ioport
  12. Bandouch, Jan; Jenkins, Odest Chadwicke; Beetz, Michael: A self-training approach for visual tracking and recognition of complex human activity patterns (2012) ioport
  13. Caseiro, Rui; Martins, Pedro; Henriques, João F.; Batista, Jorge: A nonparametric Riemannian framework on tensor field with application to foreground segmentation (2012)
  14. Deepak, K. Sai; Medathati, N. V. Kartheek; Sivaswamy, Jayanthi: Detection and discrimination of disease-related abnormalities based on learning normal cases (2012) ioport
  15. Heidary, Kaveh; Caulfield, H. John: Needles in a haystack: fast spatial search for targets in similar-looking backgrounds (2012)
  16. Lim, Taegyu; Han, Bohyung; Han, Joon H.: Modeling and segmentation of floating foreground and background in videos (2012)
  17. Marti-Puig, Pere; Rodríguez, Sara; De Paz, Juan F.; Reig-Bolaño, Ramon; Rubio, Manuel P.; Bajo, Javier: Stereo video surveillance multi-agent system: new solutions for human motion analysis (2012)
  18. Yao, Anbang; Lin, Xinggang; Wang, Guijin; Yu, Shan: A compact association of particle filtering and kernel based object tracking (2012)
  19. Zhang, Canlong; Jing, Zhongliang; Jin, Bo; Li, Zhixin: A dual-kernel-based tracking approach for visual target (2012) ioport
  20. Assheton, P.; Hunter, A.: A shape-based voting algorithm for pedestrian detection and tracking (2011)

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