Propagating Certainty in Petri Nets for Activity Recognition.

This paper considers the problem of recognizing activities as they occur in surveillance video. Activities are high-level nonatomic semantic concepts which may have complex temporal structure. Activities are not easily identifiable using image features, but rather by the recognition of their composing events. Unfortunately, these composing events may only be observed up to a particular certainty. This paper describes particle filter Petri Net (PFPN), an activity recognition process that combines uncertain event observations to determine the likelihood that a particular activity is taking place in a video sequence. Our paper is based on previous study in which activities are specified as Petri Nets. The stochastic PFPN framework proposed in this paper improves over existing deterministic approaches to activity recognition by enabling the certainty reasoning required for coping with inherent ambiguity in both low-level video processing and activity definition. Furthermore, the PFPN approach reduces the dependence on a duration model and enables the creation of holistic activity models. Often when activity recognition frameworks are proposed they are strongly paired with a particular methodology for low-level video processing and event recognition. Each proposed approach is then applied to a nonstandard dataset. In our experiments, we provide an empirical comparison of our approach with leading activity recognition approaches across several datasets, using a constant event recognition as input. Our results illustrate the tradeoff between deterministic and stochastic activity recognition approaches. Furthermore, our experiments suggest that the holistic PFPN approach is more robust for activity recognition in the surveillance video domain than competing approaches. [ABSTRACT FROM AUTHOR]