We present a system for recognising human activity given a symbolic representation of video content. The input of our system is a set of time-stamped short-term activities (STA) detected on video frames. The output is a set of recognised long-term activities (LTA), which are pre-dened temporal combinations of STA. The constraints on the STA that, if satised, lead to the recognition of a LTA, have been expressed using a dialect of the Event Calculus. In order to handle the uncertainty that naturally occurs in human activity recognition, we adapted this dialect to a state-of-the-art probabilistic logic programming framework. We present a detailed evaluation and comparison of the crisp and probabilistic approaches through experimentation on a benchmark dataset of human surveillance videos.
ProbLog and MLN are closely related. A notable difference between them is that MLN, as an extension of first-order logic, are not bound by the closed-world assumption. There exists a body of work that investigates the connection between the two frameworks. @cite_0 , for example, developed an extension of ProbLog which is able to handle first-order formulas with weighted constraints. Fierens et. al weightedcnfs converted probabilistic logic programs to ground MLN and then used state-of-the-art MLN inference algorithms to perform inference on the transformed programs.
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We introduce First Order ProbLog, an extension of first order logic with soft constraints where formulas are guarded by probabilistic facts. The paper defines a semantics for FOProbLog, develops a translation into ProbLog, a system that allows a user to compute the probability of a query in a similar setting restricted to Horn clauses, and reports on initial experience with inference.