• Publications
  • Influence
Adaptive background mixture models for real-time tracking
  • C. Stauffer, W. Grimson
  • Mathematics, Computer Science
  • Proceedings. IEEE Computer Society Conference on…
  • 23 June 1999
This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model, resulting in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes. Expand
Learning Patterns of Activity Using Real-Time Tracking
This paper focuses on motion tracking and shows how one can use observed motion to learn patterns of activity in a site and create a hierarchical binary-tree classification of the representations within a sequence. Expand
Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models
A novel unsupervised learning framework to model activities and interactions in crowded and complicated scenes with many kinds of activities co-occurring, and three hierarchical Bayesian models are proposed that advance existing language models, such as LDA and HDP. Expand
Gait analysis for recognition and classification
  • L. Lee, W. Grimson
  • Computer Science
  • Proceedings of Fifth IEEE International…
  • 20 May 2002
This work describes a representation of gait appearance based on simple features such as moments extracted from orthogonal view video silhouettes of human walking motion that contains enough information to perform well on human identification and gender classification tasks. Expand
Using adaptive tracking to classify and monitor activities in a site
A vision system that monitors activity in a site over extended periods of time using tracked motion data to calibrate the distributed sensors, to construct rough site models, to classify detected objects, to learn common patterns of activity for different object classes, and to detect unusual activities. Expand
Object recognition by computer - the role of geometric constraints
This book describes an extended series of experiments into the role of geometry in the critical area of object recognition, providing precise definitions of the recognition and localization problems, the methods used to address them, the solutions to these problems, and the implications of this analysis. Expand
Spatial Latent Dirichlet Allocation
A topic model Spatial Latent Dirichlet Allocation (SLDA), which better encodes spatial structures among visual words that are essential for solving many vision problems, is proposed and used to discover objects from a collection of images. Expand
Learning Semantic Scene Models by Trajectory Analysis
An unsupervised learning framework to segment a scene into semantic regions and to build semantic scene models from long-term observations of moving objects in the scene is described and novel clustering algorithms which use both similarity and comparison confidence are introduced. Expand
Trajectory analysis and semantic region modeling using a nonparametric Bayesian model
A novel nonparametric Bayesian model for trajectory analysis and semantic region modeling in surveillance settings, in an unsupervised way, that advances the existing hierarchical Dirichlet processes (HDP) language model. Expand