Human motion trajectories, however captured, provide a rich spatiotemporal data source for human activity recognition, and the rich literature in motion trajectory analysis provides the tools to bridge the gap between this data and its semantic interpretation. But activity is an ambiguous term across research communities. For example, in urban transport research activities are generally characterized around certain locations assuming the opportunities and resources are present in that location, and traveling happens between these locations for activity participation, i.e., travel is not an activity, rather a mean to overcome spatial constraints. In contrast, in human-computer interaction (HCI) research and in computer vision research activities taking place "along the way," such as "reading on the bus," are significant for contextualized service provision. Similarly activities at coarser spatial and temporal granularity, e.g., "holidaying in a country," could be recognized in some context or domain. Thus the context prevalent in the literature does not provide a precise and consistent definition of activity, in particular in differentiation to travel when it comes to motion trajectory analysis. Hence in this paper, a thorough literature review studies activity from different perspectives, and develop a common framework to model and reason human behavior flexibly across contexts. This spatio-temporal framework is conceptualized with a focus on modeling activities hierarchically. Three case studies will illustrate how the semantics of the term activity changes based on scale and context. They provide evidence that the framework holds over different domains. In turn, the framework will help developing various applications and services that are aware of the broad spectrum of the term activity across contexts.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Das, Rahul Deb and Winter, Stephan
"A context-sensitive conceptual framework for activity modeling,"
Journal of Spatial Information Science:
Available at: https://digitalcommons.library.umaine.edu/josis/vol2016/iss12/3