Title :
Spatio-temporal multimodal mean
Author :
Azmat, Shoaib ; Wills, Linda ; Wills, Sandra
Author_Institution :
Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
Abstract :
Existing background modeling techniques that model objects that have become stationary will incorrectly detect a new object if an existing stationary object is moved. A novel spatio-temporal reasoning mechanism is presented based on multi-layer background modeling and appearance model of the displaced object to conserve state of the objects in a scene. The mechanism models layers of the foreground objects that have become stationary, along with moving foreground and background. An object that changes its place, partially or fully, is recognized based on its appearance model. The end result is the correct modeling of objects even in the case of spatial displacements. This provides a richer mechanism of analyzing and visualizing spatio-temporal scene events than the traditional binary foreground/background segmentation.
Keywords :
data visualisation; image motion analysis; image segmentation; inference mechanisms; object detection; video signal processing; video surveillance; appearance model; binary foreground/background segmentation; foreground objects; moving background; moving foreground; multilayer background modeling; object detection; object modeling; spatial displacements; spatio-temporal multimodal mean; spatio-temporal reasoning mechanism; spatio-temporal scene events visualization; stationary object; video summarization; video surveillance; Adaptation models; Cognition; Color; Computational modeling; Histograms; Image color analysis; Surveillance; appearance model; multilayer background modeling; scene understanding; spatio-temporal reasoning; video summarization; video surveillance;
Conference_Titel :
Image Analysis and Interpretation (SSIAI), 2014 IEEE Southwest Symposium on
Conference_Location :
San Diego, CA
DOI :
10.1109/SSIAI.2014.6806034