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V.: Multi-level background initialization using hidden markov models (2003)
| Content Provider | CiteSeerX |
|---|---|
| Author | Cristani, Marco Bicego, Manuele Murino, Vittorio |
| Description | Most of the automated video-surveillance applications are based on the process of background modelling, aimed at discriminating motion patterns of interest at pixel, region or frame level in a nearly static scene. The issues character-izing an ordinary background modelling process are typically three: the background model representation, the initializa-tion, and the adaptation. This paper proposes a novel ini-tialization algorithm, able to bootstrap an integrated pixel-and region-based background modelling algorithm. The in-put is an uncontrolled video sequence in which moving ob-jects are present, the output is a pixel- and region-level sta-tistical background model describing the static information of a scene. At the pixel level, multiple hypotheses of the background values are generated by modelling the intensity of each pixel with a Hidden Markov Model (HMM), also cap-turing the sequentiality of the di®erent color (or gray-level) intensities. At the region level, the resulting HMMs are clus-tered with a novel similarity measure, able to remove moving objects from a sequence, and obtaining a segmented image of the observed scene, in which each region is characterized by a similar spatio-temporal evolution. Experimental trials on synthetic and real sequences have shown the e®ectiveness of the proposed approach. |
| File Format | |
| Language | English |
| Publisher | ACM |
| Publisher Date | 2003-01-01 |
| Publisher Institution | In: First ACM SIGMM international workshop on Video surveillance, IWVS ’03 |
| Access Restriction | Open |
| Subject Keyword | Novel Similarity Measure Ordinary Background Modelling Process Real Sequence Frame Level Background Model Representation Background Modelling Novel Ini-tialization Algorithm Region Level Background Value Multi-level Background Initialization Static Scene Region-level Sta-tistical Background Model Hidden Markov Model Video-surveillance Application Di Erent Color Integrated Pixel-and Region-based Background Segmented Image Motion Pattern Static Information Pixel Level Observed Scene Multiple Hypothesis Similar Spatio-temporal Evolution Experimental Trial Uncontrolled Video Sequence |
| Content Type | Text |
| Resource Type | Article |
