No abstract

No abstract

A methodology to classify abandoned objects in video surveillance environments is proposed. Our aim is to determine a set of relevant features that properly describes the main patterns of the objects. Assuming that the abandoned object was previously detected by a visual surveillance framework, a preprocessing stage to segment the region of interest from a given detected object is also presented. Then, some geometric and Hu's moments features are estimated. Moreover, a relevance analysis is employed to identify which features reveal the major variability of the input space to discriminate among different objects. Attained results over a real-world video surveillance dataset show how our approach is able to select a subset of features for achieving stable classification performance. Our approach seems to be a good alternative to support the development of automated video surveillance systems.

No abstract

No abstract