Alessia Saggese

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The psychological overcharge issue related to human inadequacy to maintain a constant level of attention in simultaneously monitoring multiple visual information sources makes necessary to develop enhanced video surveillance systems that automatically understand human behaviors and identify dangerous situations. This paper introduces a semantic human(More)
In this paper we propose a novel real-time tracking algorithm robust with respect to several common errors occurring in object detection systems, especially in the presence of total or partial occlusions. The algorithm takes into account the history of each object, whereas most other methods base their decisions on only the last few frames. More precisely,(More)
—This work aims to identify abnormal behaviors from the analysis of humans or vehicles' trajectories. A set of normal trajectories' prototypes is extracted by means of a novel unsupervised learning technique: the scene is adaptively partitioned into zones by using the distribution of the training set and each trajectory is represented as a sequence of(More)
In this paper we propose a novel method for recognizing human actions by exploiting a multi-layer representation based on a deep learning based architecture. A first level feature vector is extracted and then a high level representation is obtained by taking advantage of a Deep Belief Network trained using a Restricted Boltzmann Machine. The classification(More)
Moving people's and objects' trajectories extracted from video sequences are increasingly assuming a key role for detecting anomalous events and for characterizing human behaviors. Among the key related issues, there is the need of efficiently storing a huge amount of 3D trajectories together with retrieval techniques sufficiently fast to allow a real-time(More)