Bayesian Generative Model Based on Color Histogram of Oriented Phase and Histogram of Oriented Optical Flow for Rare Event Detection in Crowded Scenes
| Autor: | Dieudonné Fabrice Atrevi, Bruno Emile, Damien Vivet |
|---|---|
| Přispěvatelé: | Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE), Institut National des Sciences Appliquées - INSA (FRANCE), Université d'Orléans (FRANCE), ATREVI, Dieudonné, Laboratoire pluridisciplinaire de recherche en ingénierie des systèmes, mécanique et énergétique (PRISME), Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Département Electronique, Optronique et Signal (DEOS), Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Color histogram
Histograms Computer science Feature extraction Optical flow ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology [INFO] Computer Science [cs] Latent Dirichlet allocation Optical imaging symbols.namesake [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Histogram 0202 electrical engineering electronic engineering information engineering [INFO]Computer Science [cs] Data mining ComputingMilieux_MISCELLANEOUS Visualization business.industry Resource management [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Optique / photonique Pattern recognition Filter (signal processing) Generative model symbols 020201 artificial intelligence & image processing Artificial intelligence Event detection business |
| Zdroj: | ICASSP 2018-2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ICASSP 2018-2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2018, Calgary, Canada. pp.3126-3130 ICASSP |
| Popis: | In this paper, we propose a new method for rare event detection in crowded scenes using a combination of Color Histogram of Oriented Phases (CHOP) and Histogram of Oriented Optical Flow (HOOF). We propose to detect and filter spatio-temporal interest points (STIP) based on the visual saliency information of the scene. Once salient STIPs are detected, the motion and appearance information of the surrounding scene are extracted. Finally, the extracted information from normal scenes are modelled by using a Bayesian generative model (Latent Dirichlet Allocation). The rare events are detected by processing the likelihood of the current scene in regard to the obtained model. The proposed method has been tested on the publicly available UMN dataset and compared with different state-of-the-art algorithms. We have shown that our method is very competitive and provides promising results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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