Probabilistic Recognition of Complex Event

International audienceAbstract. This paper describes a complex event recognition approach with probabilistic reasoning for handling uncertainty. The first advantage of the proposed approach is the flexibility of the modeling of composite events with complex temporal constraints. The second advantage is the use of probability theory providing a consistent framework for dealing with uncertain knowledge for the recognition of complex events. The experimental results show that our system can successfully improve the event recognition rate. We conclude by comparing our algorithm with the state of the art and showing how the definition of event models and the probabilistic reasoning can influence the results of the real-time event recognition

ASPIE: A Framework for Active Sensing and Processing of Complex Events in the Internet of Manufacturing Things

Rapid perception and processing of critical monitoring events are essential to ensure healthy operation of Internet of Manufacturing Things (IoMT)-based manufacturing processes. In this paper, we proposed a framework (active sensing and processing architecture (ASPIE)) for active sensing and processing of critical events in IoMT-based manufacturing based on the characteristics of IoMT architecture as well as its perception model. A relation model of complex events in manufacturing processes, together with related operators and unified XML-based semantic definitions, are developed to effectively process the complex event big data. A template based processing method for complex events is further introduced to conduct complex event matching using the Apriori frequent item mining algorithm. To evaluate the proposed models and methods, we developed a software platform based on ASPIE for a local chili sauce manufacturing company, which demonstrated the feasibility and effectiveness of the proposed methods for active perception and processing of complex events in IoMT-based manufacturing

Activity Recognition and Uncertain Knowledge in Video Scenes

International audienceActivity recognition has been a growing research topic in the last years and its application varies from auto-matic recognition of social interaction such as shaking hands, parking lot surveillance, traffic monitoring and the detection of abandoned luggage. This paper describes a probabilistic framework for uncertainty handling in a description-based event recognition approach. The proposed approach allows the flexible modeling of composite events with complex temporal constraints. It uses probability theory to provide a consistent framework for dealing with uncertain knowledge for the recognition of complex events. We validate the event recognition accuracy of the proposed algorithm on real-world videos. The experimental results show that our system can successfully recognize activities with a high recognition rate. We conclude by comparing our algorithm with the state of the art and showing how the definition of event models and the probabilistic reasoning can influence the results of real-time event recognitio