Multilayer background modeling under occlusions for spatio-temporal scene analysis

Video surveillance;Multilayer background modeling;Adaptive background modeling;Temporal multimodal mean;Occlusion reasoning;Appearance model;Video summarization;Low-power integrated GPU;Embedded smart camera;Spatio-temporal scene analysis

Azmat, Shoaib ; Wills, Linda Wills, Scott Electrical and Computer Engineering Hamblen, James Hong, Bo Lanterman, Aaron Vetter, Jeffrey ; Wills, Linda

University:Georgia Institute of Technology

Department:Electrical and Computer Engineering

关键词: Video surveillance;    Multilayer background modeling;    Adaptive background modeling;    Temporal multimodal mean;    Occlusion reasoning;    Appearance model;    Video summarization;    Low-power integrated GPU;    Embedded smart camera;    Spatio-temporal scene analysis;   

Others  :  https://smartech.gatech.edu/bitstream/1853/54005/1/AZMAT-DISSERTATION-2014.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

This dissertation presents an efficient multilayer background modeling approach to distinguish among midground objects, the objects whose existence occurs over varying time scales between the extremes of short-term ephemeral appearances (foreground) and long-term stationary persistences (background). Traditional background modeling separates a given scene into foreground and background regions. However, the real world can be much more complex than this simple classification, and object appearance events often occur over varying time scales. There are situations in which objects appear on the scene at different points in time and become stationary; these objects can get occluded by one another, and can change positions or be removed from the scene. Inability to deal with such scenarios involving midground objects results in errors, such as ghost objects, miss-detection of occluding objects, aliasing caused by the objects that have left the scene but are not removed from the model, and new objects’ detection when existing objects are displaced. Modeling temporal layers of multiple objects allows us to overcome these errors, and enables the surveillance and summarization of scenes containing multiple midground objects.