Articulated object tracking by rendering consistent appearance parts

We describe a general methodology for tracking 3-dimensional objects in monocular and stereo video that makes use of GPU-accelerated filtering and rendering in combination with machine learning techniques. The method operates on targets consisting of kinematic chains with known geometry. The tracked target is divided into one or more areas of consistent appearance. The appearance of each area is represented by a classifier trained to assign a class-conditional probability to image feature vectors. A search is then performed on the configuration space of the target to find the maximum likelihood configuration. In the search, candidate hypotheses are evaluated by rendering a 3D model of the target object and measuring its consistency with the class probability map. The method is demonstrated for tool tracking on videos from two surgical domains, as well as in a human hand-tracking task.

Authors

• Zachary A. Pezzementi
• Sandrine Voros
• Gregory D. Hager

Keywords

• Target tracking
• Image edge detection
• Humans
• Solid modeling
• Kinematics
• Rendering (computer graphics)
• Information geometry
• Surgery
• Robots
• Histograms
• Probability Function
• Class Probabilities
• Configuration Space
• Tracking Tool
• Hand Tracking
• Degrees Of Freedom
• Objective Function
• Simulated Data
• Maximum Likelihood Estimation
• Images Of Samples
• Linear Discriminant Analysis
• Mixture Model
• Simulated Datasets
• Likelihood Function
• Texture Features
• Laparoscopic Surgery
• Particle Filter
• Depth Of Field
• Final Configuration
• Simulated Sequences
• Retinal Surgery
• Configuration Of Objects
• GPU Implementation
• Nelder-Mead Algorithm
• Needle Holder
• Video Sequences
• Linear Dimensionality Reduction
• Single Class