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Báo cáo hóa học: " Research Article Robust Abandoned Object Detection Using Dual Foregrounds"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Robust Abandoned Object Detection Using Dual Foregrounds | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008 Article ID 197875 11 pages doi 10.1155 2008 197875 Research Article Robust Abandoned Object Detection Using Dual Foregrounds Fatih Porikli 1 Yuri Ivanov 1 and Tetsuji Haga2 1 Mitsubishi Electric Research Labs MERL 201 Broadway Cambridge MA 02139 USA 2 Mitsubishi Electric Corp. Advanced Technology R D Center Amagasaki 661-8661 Hyogo Japan Correspondence should be addressed to Fatih Porikli fatih@merl.com Received 25 January 2007 Accepted 28 August 2007 Recommended by Enis Ahmet Cetin As an alternative to the tracking-based approaches that heavily depend on accurate detection of moving objects which often fail for crowded scenarios we present a pixelwise method that employs dual foregrounds to extract temporally static image regions. Depending on the application these regions indicate objects that do not constitute the original background but were brought into the scene at a subsequent time such as abandoned and removed items illegally parked vehicles. We construct separate long- and short-term backgrounds that are implemented as pixelwise multivariate Gaussian models. Background parameters are adapted online using a Bayesian update mechanism imposed at different learning rates. By comparing each frame with these models we estimate two foregrounds. We infer an evidence score at each pixel by applying a set of hypotheses on the foreground responses and then aggregate the evidence in time to provide temporal consistency. Unlike optical flow-based approaches that smear boundaries our method can accurately segment out objects even if they are fully occluded. It does not require on-site training to compensate for particular imaging conditions. While having a low-computational load it readily lends itself to parallelization if further speed improvement is necessary. Copyright 2008 Fatih Porikli et al. This is an open access article distributed under the Creative Commons Attribution