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Detection of Temporarily Static Regions by Processing Video at Different Frame (2007)
| Content Provider | CiteSeerX |
|---|---|
| Author | Porikli, Fatih |
| Abstract | This paper presents an abandoned item and illegally parked vehicle detection method for single static camera video surveillance applications. By processing the input video at different frame rates, two backgrounds are constructed; one for short-term and another for long-term. Each of these backgrounds is defined as a mixture of Gaussian models, which are adapted using online Bayesian update. Two binary foreground maps are estimated by comparing the current frame with the backgrounds, and motion statics are aggregated in a likelihood image by applying a set of heuristics to the foreground maps. Likelihood image is then used to differentiate between the pixels that belong to moving objects, temporarily static regions and scene background. Depending on the application, the temporary static regions indicate abandoned items, illegally parked vehicles, objects removed from the scene, etc. The presented pixel-wise method does not require object tracking, thus its performance is not upper-bounded to error prone detection and correspondence tasks that usually fail for crowded scenes. It accurately segments objects even if they are fully occluded. It can also be effectively implemented on a parallel processing architecture. |
| File Format | |
| Publisher Date | 2007-01-01 |
| Access Restriction | Open |
| Subject Keyword | Different Frame Temporarily Static Region Likelihood Image Input Video Gaussian Model Abandoned Item Binary Foreground Map Correspondence Task Parallel Processing Architecture Presented Pixel-wise Method Prone Detection Motion Static Scene Background Object Tracking Parked Vehicle Detection Method Online Bayesian Update Current Frame Static Region Parked Vehicle Temporary Static Region Different Frame Rate Foreground Map |
| Content Type | Text |
| Resource Type | Article |
