Publications

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This paper presents Sakbot, a system for moving object detection and tracking in traffic monitoring and video surveillance applications. The system is endowed with robust and efficient detection techniques, which main features are the statistical and knowledge-based background update and the use of HSV color information for shadow suppression. Tracking is performed by means of a flexible tracking module based on symbolic reasoning, which can be tuned to several different applications.

Shadow detection is critical for robust and reliable vision-based systems for traffic flow analysis. In this paper we discuss various shadow detection approaches and compare two critically. The goal of these algorithms is toprevent moving shadows being misclassified as moving objects (or parts of them), thus avoiding the merging of twoor more objects into one and improving the accuracy of object localization. The environment considered is an outdoorhighway scene with multiple lanes observed by a single fixedcamera. The important features of shadow detection algorithms and the parameter set-up are analyzed and discussed. A critical evaluation of the results both in terms of accuracy and in terms of computational complexity are outlined. Finally, possible integration of the two approaches into a robust shadow detector is presented as future direction of our research.

This paper presents Sakbot, a system for moving object detection in traffic monitoring and video surveillance applications. The system is endowed with robust and efficient detection techniques, which main features are the statistical and knowledge-based background update and the use of HSV color information for shadow suppression. Tracking is provided by a symbolic reasoning module allowing flexible object tracking over a variety of different applications. This system proves effective on many different situations, both from the point of view of the scene appearance and the purpose of the application.

Image analysis tools are spreading in dermatology since the introduction of dermoscopy (epiluminescence microscopy), in the effort of algorithmically reproducing clinical evaluations. Color-based region segmentation of skin lesions is one of the key steps for correctly collecting statistics that can help clinicians in their diagnosis. Nevertheless, an efficient and accurate region segmentation algorithm has not been proposed in the literature yet. This work proposes an iterative fuzzy c-means clustering algorithm based on PCA with the Karhunen-Loève transform of the color space. A topological tree is provided to store the mutual inclusions of the regions and then used to summarize the structural properties of the skin lesion. Preliminary experimental results are presented and discussed.

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The most common approach used for vision-based traffic surveillance consists of a fast segmentation of moving visual objects (MVOs) in the scene together with an intelligent reasoning module capable of identifying, tracking and classifying the MVOs in dependency of the system goal. In this paper we describe our approach for MVOs segmentation in an unstructured traffic environment. We consider complex situations with moving people, vehicles and infrastructures that have different aspect model and motion model. In this case we define a specific approach based on background subtraction with statistic and knowledge-based background update. We show many results of real-time tracking of traffic MVOs in outdoor traffic scene such as roads, parking area intersections, and entrance with barriers