Abnormal Events Detection Based on Trajectory Clustering
| Autor: | Emna Fendri, Najla Bouarada Ghrab, Mohamed Hammami |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
050210 logistics & transportation
business.industry 05 social sciences Correlation clustering Feature extraction Pattern recognition 02 engineering and technology Similarity measure computer.software_genre ComputingMethodologies_PATTERNRECOGNITION Trajectory clustering Robustness (computer science) CURE data clustering algorithm 0502 economics and business 0202 electrical engineering electronic engineering information engineering Cluster (physics) 020201 artificial intelligence & image processing Artificial intelligence Data mining business Cluster analysis computer Mathematics |
| Zdroj: | 2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV). |
| DOI: | 10.1109/cgiv.2016.65 |
| Popis: | Trajectories of moving objects provide crucial clues for video event analysis especially in surveillance applications. In this paper, we proposed a novel approach for detecting abnormal events in video surveillance. Our approach is based on trajectory analysis involving two phases. In the first phase, we extracted clusters of normal events through an agglomerative hierarchical clustering of saved trajectories that were of different lengths, of different local time shifts and containing noise. Then, for each cluster a model was established. In the second phase, we aimed to classify a new event as normal or abnormal one. To achieve this objective, a comparison was performed with the extracted clusters' models thereby reducing the complexity and accelerating the classification process. Experiments were conducted to demonstrate the efficacy and the performance of our approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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