A Synchronous Approach to Activity Recognition

Abstract : Activity Recognition aims at recognizing and understanding sequences of actions and movements of mobile objects (human beings, animals or artefacts), that follow the predefined model of an activity. We propose to describe activities as a series of actions, triggered and driven by environmental events. Due to the large range of application domains (surveillance, safety, health care...), we propose a generic approach to design activity recognition systems that interact continously with their environment and react to its stimuli at run-time. In our target applications, the data coming from sensors (video-cameras, etc.) are first processed to recognize and track objects and to detect low-level events. This low-level information is collected and transformed into higher level inputs to our activity recognition system. Such recognition systems must satisfy stringent requirements: dependability, real time, cost effectiveness , security and safety, correctness, completeness... To enforce most of these properties our approach is to base the configuration of the system as well as its execution on formal techniques. We chose the synchronous approach which provides formal bases to perform static analysis, verification and validation, but also direct implementation. Several synchronous languages such as Lustre, Esterel, Scade and Signal [2] have been defined to describe synchronous automata. These languages are for expert users. We propose a new user-oriented language , named ADeL (Activity Description Language) to express activities and to automatically generate recognition automata. This language is easier to understand and to use by non computer scientists (e.g., physicians) while relying on formal semantics.
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Submitted on : Thursday, November 22, 2018 - 4:34:48 PM
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Ines Sarray, Annie Ressouche, Sabine Moisan, Jean-Paul Rigault, Daniel Gaffé. A Synchronous Approach to Activity Recognition. IEEE 12th International Conference on Semantic Computing (ICSC), Jan 2018, Laguna Hills, CA, United States. ⟨hal-01931315⟩

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