Clustering Based on Two Layers for Abnormal Event Detection in Video Surveillance
| Autor: | Mohamed Hammami, Emna Fendri, Najla Bouarada Ghrab |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
ComputingMethodologies_PATTERNRECOGNITION
Artificial Intelligence Computer Networks and Communications Computer science business.industry Event (relativity) Pattern recognition Artificial intelligence Cluster analysis business Computer Graphics and Computer-Aided Design Software Computer Science Applications |
| Zdroj: | International Journal of Software Innovation. 5:1-18 |
| ISSN: | 2166-7179 2166-7160 |
| DOI: | 10.4018/ijsi.2017100101 |
| Popis: | Abnormal event detection has attracted great research attention in video surveillance. In this paper, the authors presented a robust method of trajectories clustering for abnormal event detection. This method is based on two layers and benefits from two well-known clustering algorithms: the agglomerative hierarchical clustering and the k-means clustering. Facing to the challenges related to the trajectories, e.g., different sizes, the authors introduce a preprocessing step to unify their sizes and reduce their dimensionality. The experimental results show the performance and accuracy of their proposed method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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