Domenico Daniele Bloisi

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Background subtraction is a widely used operation in the video surveillance, aimed at separating the expected scene (the background) from the unexpected entities (the foreground). There are several problems related to this task, mainly due to the blurred boundaries between background and foreground definitions. Therefore, background subtraction is an open(More)
Visual surveillance in dynamic scenes is currently one of the most active research topics in computer vision and many existing applications are available. However, difficulties in realizing effective video surveillance systems that are robust to the many different conditions that arise in real environments, make the actual deployment of such systems very(More)
Maritime environment represents a challenging scenario for automatic video surveillance due to the complexity of the observed scene: waves on the water surface, boat wakes, and weather issues contribute to generate a highly dynamic background. Moreover, an appropriate background model has to deal with gradual and sudden illumination changes, camera jitter,(More)
In this paper an automatic maritime surveillance system is presented. Boat detection is performed by means of an Haar-like classifier in order to obtain robustness with respect to targets having very different size, reflections and wakes on the water surface, and apparently motionless boats anchored off the coast. Detection results are filtered over the(More)
Visual tracking of multiple targets is a key step in surveillance scenarios, far from being solved due to its intrinsic ill-posed nature. In this paper, a comparison of MultiHypothesis Kalman Filter and Particle Filter-based tracking is presented. Both methods receive input from a novel online background subtraction algorithm. The aim of this work is to(More)
The use of identical robots in the RoboCup Standard Platform League (SPL) made software development the key aspect to achieve good results in competitions. In particular, the visual detection process is crucial for extracting information about the environment. In this paper, we present a novel approach for object detection and classification based on(More)
We propose an adaptive tracking system for assisted living that integrates user information about emergency events. Information fusion between user data and visual data is performed in order to estimate and assess the situation at hand. The system is able to dynamically switch between different segmentation and tracking algorithms improving its performance,(More)