The goal of this research is to model an “activity” performed by a group of moving and interacting objects (which can be people or cars or robots or different rigid components of the human body) and use these models for abnormal activity detection, tracking and segmentation. Previous approaches to modeling group activity include co-occurrence statistics (individual and joint histograms) and Dynamic Bayesian Networks, neither of which is applicable when the number of interacting objects is large. We treat the objects as point objects (referred to as “landmarks”) and propose to model their changing configuration as a moving and deforming…
Author: Vaswani, Namrata
Source: University of Maryland
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