Stefano Maludrottu

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This paper proposes a computationally efficient approach for estimating camera motion based on a voting mechanism that neither relies on explicit feature matching nor RANSAC. In the presented method, correspondences arise as a consequence of finding the camera movement in a voting space and not as a prerequisite. Experiments show promising results compared(More)
Real-time automatic human behavior recognition is one of the most challenging tasks for intelligent surveillance systems. Its importance lies in the possibility of robust detection of suspicious behaviors in order to prevent possible threats. The widespread integration of tracking algorithms into modern surveillance systems makes it possible to acquire(More)
The process of finding representative shape patterns from sparse datasets is a challenging task: especially for non-rigid objects, shape deformations through time can produce very different sets of corners from frame to frame and a proper comparison of point features can be very difficult. Evaluating a multi-objective fitness function in a discrete voting(More)
In this paper a joint human tracking and human-to-human interaction recognition system is proposed. While usually these two functions are performed separately, it will be shown that it is possible to improve the tracking performances if these functions are done jointly. For this purpose, a Bayesian tracking algorithm is coupled with a bio-inspired(More)
A correct video segmentation, namely the detection of moving objects within a scene plays a very important role in many application in safety, surveillance, trafic monitoring and object detection. The main objective of this paper is to implement an effective background segmentation algorithm for corner sets extracted from video sequences. A dynamic(More)
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