Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion
International Journal of Computer Vision
Example-Based Object Detection in Images by Components
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graphical Models: Foundations of Neural Computation
Graphical Models: Foundations of Neural Computation
A Probabilistic Approach to Object Recognition Using Local Photometry and Global Geometry
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Finding Deformable Shapes Using Loopy Belief Propagation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
PAMPAS: real-valued graphical models for computer vision
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Tracking by parts: a Bayesian approach with component collaboration
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
SAMT'10 Proceedings of the 5th international conference on Semantic and digital media technologies
Loose-limbed People: Estimating 3D Human Pose and Motion Using Non-parametric Belief Propagation
International Journal of Computer Vision
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We present a probabilistic framework for component-based automatic detection and tracking of objects in video. We represent objects as spatio-temporal two-layer graphical models, where each node corresponds to an object or component of an object at a given time, and the edges correspond to learned spatial and temporal constraints. Object detection and tracking is formulated as inference over a directed loopy graph, and is solved with non-parametric belief propagation. This type of object model allows object-detection to make use of temporal consistency (over an arbitrarily sized temporal window), and facilitates robust tracking of the object. The two layer structure of the graphical model allows inference over the entire object as well as individual components. AdaBoost detectors are used to define the likelihood and form proposal distributions for components. Proposal distributions provide 'bottom-up' information that is incorporated into the inference process, enabling automatic object detection and tracking. We illustrate our method by detecting and tracking two classes of objects, vehicles and pedestrians, in video sequences collected using a single grayscale uncalibrated car-mounted moving camera.