TAILIEUCHUNG - Event Modeling and Recognition using Markov Logic Networks ?

Hadoop has a master-slave architecture (Figure 2), with a unique master host and multiple slave hosts, typ- ically configured as follows. The master host runs two daemons: (1) the JobTracker, which schedules and man- ages all of the tasks belonging to a running job; and (2) the NameNode, which manages the HDFS namespace, and regulates access to files by clients (which are typi- cally the executing tasks). Each slave host runs two daemons: (1) the Task- Tracker, which launches tasks on its host, based on in- structions from the JobTracker; the TaskTracker also keeps track of the progress of each task on its host; and (2) the DataNode, which serves data blocks. | Event Modeling and Recognition using Markov Logic Networks Son D. Tran and Larry S. Davis Department of Computer Science University of Maryland College Park MD 20742 USA sontran lsd @ Abstract. We address the problem of visual event recognition in surveillance where noise and missing observations are serious problems. Common sense domain knowledge is exploited to overcome them. The knowledge is represented as first-order logic production rules with associated weights to indicate their confidence. These rules are used in combination with a relaxed deduction algorithm to construct a network of grounded atoms the Markov Logic Network. The network is used to perform probabilistic inference for input queries about events of interest. The system s performance is demonstrated on a number of videos from a parking lot domain that contains complex interactions of people and vehicles. 1 Introduction We consider the problem of event modelling and recognition in visual surveillance and introduce an approach based on Markov Logic Networks 1 that naturally integrates common sense reasoning with uncertain analyses produced by computer vision algorithms for object detection tracking and movement recognition. We motivate and illustrate our approach in the context of monitoring a parking lot with the goal of matching people to the vehicles they arrive and depart in. There are numerous frameworks for event recognition. In declarative approaches . 2 events are represented with declarative templates. Events are typically organized in a hierarchy starting with primitive events at the bottom and composite events on top. The recognition of a composite event proceeds in a bottom-up manner. These approaches have several drawbacks. First a miss or false detection of a primitive event which occurs frequently in computer vision especially in crowded or poorly illuminated conditions often leads to irrecoverable failures in composite event recognition. Second uncertainty is often not

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