BraMBLe

BraMBLe: A Bayesian multiple blob tracker. Blob trackers have become increasingly powerful in recent years largely due to the adoption of statistical appearance models which allow effective background subtraction and robust tracking of deforming foreground objects. It has been standard, however, to treat background and foreground modelling as separate processes-background subtraction is followed by blob detection and tracking-which prevents a principled computation of image likelihoods. This paper presents two theoretical advances which address this limitation and lead to a robust multiple-person tracking system suitable for single-camera real-time surveillance applications. The first innovation is a multi-blob likelihood function which assigns directly comparable likelihoods to hypotheses containing different numbers of objects. This likelihood function has a rigorous mathematical basis: it is adapted from the theory of Bayesian correlation, but uses the assumption of a static camera to create a more specific background model while retaining a unified approach to background and foreground modelling. Second we introduce a Bayesian filter for tracking multiple objects when the number of objects present is unknown and varies over time. We show how a particle filter can be used to perform joint inference on both the number of objects present and their configurations. Finally we demonstrate that our system runs comfortably in real time on a modest workstation when the number of blobs in the scene is small.


References in zbMATH (referenced in 27 articles )

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  1. Xie, Yuan; Zhang, Wensheng; Qu, Yanyun; Zhang, Yinghua: Discriminative subspace learning with sparse representation view-based model for robust visual tracking (2014)
  2. Faux, Francis; Luthon, Franck: Theory of evidence for face detection and tracking (2012) ioport
  3. Hoseinnezhad, Reza; Vo, Ba-Ngu; Vo, Ba-Tuong; Suter, David: Visual tracking of numerous targets via multi-Bernoulli filtering of image data (2012) ioport
  4. Keck, Mark; Davis, James W.: Recovery and reasoning about occlusions in 3D using few cameras with applications to 3D tracking (2011) ioport
  5. Eshel, Ran; Moses, Yael: Tracking in a dense crowd using multiple cameras (2010) ioport
  6. Goyat, Y.; Chateau, T.; Trassoudaine, L.: Tracking of vehicle trajectory by combining a camera and a laser rangefinder (2010) ioport
  7. Leykin, Alex; Hammoud, Riad: Pedestrian tracking by fusion of thermal-visible surveillance videos (2010) ioport
  8. Rowe, D.; Gonzàlez, J.; Pedersoli, M.; Villanueva, J. J.: On tracking inside groups (2010) ioport
  9. Sugandi, Budi; Kim, Hyoungseop; Tan, Joo Koi; Ishikawa, Seiji: A color-based particle filter for multiple object tracking in an outdoor environment (2010) ioport
  10. Wu, Mingjun; Peng, Xianrong; Zhang, Qiheng; Zhao, Rujin: Patches-based Markov random field model for multiple object tracking under occlusion (2010)
  11. Huang, Kuang-Man; Cosman, Pamela; Schafer, William R.: Using articulated models for tracking multiple C. Elegans in physical contact (2009) ioport
  12. Wang, Xuan-He; Liu, Ji-Lin: Tracking multiple people under occlusion and across cameras using probabilistic models (2009)
  13. Dee, Hannah M.; Velastin, Sergio A.: How close are we to solving the problem of automated visual surveillance? (2008) ioport
  14. Haering, Niels; Venetianer, Péter L.; Lipton, Alan: The evolution of video surveillance: an overview (2008) ioport
  15. Mittal, Anurag; Davis, Larry S.: A general method for sensor planning in multi-sensor systems: Extension to random occlusion (2008) ioport
  16. Zhao, Tao; Aggarwal, Manoj; Germano, Thomas; Roth, Ian; Knowles, Alexandar; Kumar, Rakesh; Sawhney, Harpreet; Samarasekera, Supun: Toward a sentient environment: real-time wide area multiple human tracking with identities (2008) ioport
  17. Han, Mei; Xu, Wei; Tao, Hai; Gong, Yihong: Multi-object trajectory tracking (2007)
  18. Tsiamyrtzis, P.; Dowdall, J.; Shastri, D.; Pavlidis, I. T.; Frank, M. G.; Ekman, P.: Imaging facial physiology for the detection of deceit (2007) ioport
  19. Wu, Bo; Nevatia, Ram: Detection and tracking of multiple, partially occluded humans by Bayesian combination of edgelet based part detectors (2007) ioport
  20. Wu, Bo; Nevatia, Ram: Detection and tracking of multiple, partially occluded humans by Bayesian combination of edgelet based part detectors (2007) ioport

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