Human activities: Handling uncertainties using fuzzy time intervals

Persons may perform an activity in many different styles, or noise may cause an identical activity to have different temporal structures. We present a robust methodology for recognition of such human activities. The recognition approach presented in this paper is able to handle person-dependent and situation-dependent uncertainties and variations of human activity executions. Our system reliably recognizes human activities with such execution variations, by semantically measuring the similarity between the observations generated by an activity execution and its optimal structure. The system detects fuzzy time intervals associated with low-level gestures of a person, and matches them hierarchically with the representation of the activity that the system is maintaining. Our system is tested for eight types of simple human interactions such as `pushing¿ and `shaking hands¿, as well as complex recursive interactions like `fighting¿ and `greeting¿. The results show that the performance of our system is superior to that of the previous systems using deterministic time intervals.