Unsupervised discovery of human activities from long-time videos

IET Computer Vision, pp. 522-530, 2015.

Cited by: 13|Bibtex|Views17|DOI:https://doi.org/10.1049/iet-cvi.2014.0311
EI WOS
Other Links: academic.microsoft.com|dblp.uni-trier.de

Abstract:

In this study, the authors propose a complete framework based on a hierarchical activity model to understand and recognise activities of daily living in unstructured scenes. At each particular time of a long-time video, the framework extracts a set of space-time trajectory features describing the global position of an observed person and ...More

Code:

Data:

Your rating :
0

 

Tags
Comments