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.

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.

In this paper, we present an algorithm for tracking objects in road traffic sequences which is based on coherent strategy. This strategy relies on two times processing. Firstly, a Short-Term Processing (STP) based on spatial analysis and multilevel region descriptors matching allows identification of objects interactions and particular objects states. Secondly, a Long-Term Processing (LTP) is applied to cope with track management issues. In fact LTP feedbacks objects and their corresponding regions in each frame to update tracked object attributes. In case of merging objects, attributes are obtained using Template matching. An experimental study by quantitative and qualitative evaluations shows that the proposed approach can deal with multiple rigid objects whose sizes vary over time. The obtained results prove that our method can provide an effective and stable road objects tracks.

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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.