Vehicle tracking on video sequences via subspace learning
| Autor: | Hasan Huseyin Sonmez, Abdulhakim Gultekin, Ali Koksal Hocaoglu, Ismail Can Buyuktepe |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Vehicle tracking system
Computer science business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Astrophysics::Cosmology and Extragalactic Astrophysics 0102 computer and information sciences 02 engineering and technology Kalman filter Tracking (particle physics) 01 natural sciences Thresholding 010201 computation theory & mathematics Computer Science::Computer Vision and Pattern Recognition Histogram 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Subspace topology |
| Zdroj: | SIU |
| DOI: | 10.1109/siu.2018.8404205 |
| Popis: | In this study, we introduce a tracking algorithm which tracks the vehicles marked by an operator on video sequences. Fast Principal Component Pursuit algorithm is used to obtain the background and foreground models with high precision. For foreground model obtained by subspace learning, a thresholding method is proposed. For the detected foreground objects, features are extracted to separate the vehicle marked by the operator from other foreground objects. Tracking of the marked object is performed by Kalman filter. The experimental results show that the proposed method is effective against dynamic backgrounds, complex scenes and occlusions. |
| Databáze: | OpenAIRE |
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