Publications

This work presents a novel people tracking approach, able to cope with frequent shape changes and large occlusions. In particular, the tracks are described by means of probabilistic masks and appearance models. Occlusions due to other tracks or due to background objects and false occlusions are discriminated. The tracking system is general enough to be applied with any motion segmentation module, it can track people interacting each other and it maintains the pixel assignment to track even with large occlusions. At the same time, the update model is very reactive, so as to cope with sudden body motion and silhouette's shape changes. Due to its robustness, it has been used in many experiments of people behavior control in indoor situations.

This paper describes our approach to real-time detection of camera motion and moving object segmentation in videos acquired from moving cameras. As far as we know, none of the proposals reported in the literature are able to meet real-time requirements. In this work, we present an approach based on a color segmentation followed by a region-merging on motion through Markov Random Fields (MRFs). The technique we propose is inspired to a work of Gelgon and Bouthemy (Pattern Recognition 33 (2000) 725-40), that has been modified to reduce computational cost in order to achieve a fast segmentation (about 10 frame per second). To this aim a modified region matching algorithm (namely Partitioned Region Matching) and an innovative arc-based MRF optimization algorithm with a suitable definition of the motion reliability are proposed. Results on both synthetic and real sequences are reported to confirm validity of our solution.

This paper proposes the use of an approach of video transcoding driven by the video content and providedwith the adaptive quantization of MPEG standards.Computer vision techniques can extract semanticsfrom videos according with user's interests: the videosemantics is exploited to adapt the video in order tomeet the device's capabilities and the user'srequirements and preserve the best quality possible. Well assessed video analysis techniques are used to segment the video into objects grouped in classes ofrelevance to which the user can assign a weight proportional to their relevance. This weight is used todecide the quantization values to be applied in theMPEG-2 encoding to each macroblock. A modified version of the PSNR (Peak Signal-to-Noise Ratio) is used as performance metric and comparativeevaluation is reported with respect to other codingstandards such as JPEG, JPEG 2000, (basic) MPEG-2, and MPEG-4. Experimental results are provided on different situations, one indoor and oneoutdoor. Keywords:Videotranscoding, adaptive quantization, motion detection

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People tracking deals with problems of shape changes, self-occlusions and track occlusions due to other interfering tracks and fixed objects that hide parts of the people shape. These problems are more critical in indoor surveillance and in particular in home automation settings, in which the need to merge information obtained form different cameras distributed around the house calls for the integration of reliable data obtained during time. Therefore, tracking algorithms should be carefully tuned to cope with occlusions and shape changes, working not only at pixel level but also at region level. In this work we provide a novel technique for object tracking, based on probabilistic masks and appearance models. Occlusions due to other tracks or due to background objects and false occlusions are discriminated. The classification of occluded regions of the track is exploited in a selective model update. The tracking system is general enough to be applied with any motion segmentation module, it can track people interacting each other and it maintains the pixel to track assignment even with large occlusions. At the same time, the model update is very reactive, so as to cope with sudden body motion and silhouette's shape changes. Due to its robustness, it has been used in different experiments of people behavior control in indoor situations.

We describe an integrated solution devised for inhouse video surveillance, to control the safety of people living in a domestic environment. The system is composed of robust moving object detection module, able to disregard shadows, a tracking module designed for large occlusion solution and of a posture detector. Shadows, large occlusions and deformable model of people are key features of inhouse surveillance. Moreover, the requirements of high speed reaction to dangerous situations and the need to implement a reliable and low cost televiewing system, led to the introduction of a new multimedia model of semantic transcoding, capable of supporting different user's requests and constraints of their devices (PDA, smart phones, ...). Our application context is the emerging area of domotics (from the Latin word domus that means "home" and informatics) and, in particular, indoor video surveillance of the house where people with some difficulties (elders and disabled people) can now live in a sufficient degree of autonomy, thanks to the strong interaction with the new technologies that can be distributed in the house with affordable costs and high reliability.

The paper presents an approach for a robust (semi-)automatic correction of radial lens distortion in images and videos. This method, based on the Hough transform, has the characteristics to be applicable also on videos from unknown cameras that, consequently, can not be a priori calibrated. We approximated the lens distortion by considering only the lower-order term of the radial distortion. Thus, the method relies on the assumption that pure radial distortion transforms straight lines into curves. The computation of the best value of the distortion parameter is performed in a multi-resolution way. The method precision depends on the scale of the multi-resolution and on the Hough space's resolution. Experiments are provided for both outdoor, uncalibrated camera and an indoor, calibrated one. The stability of the value found in different frames of the same video demonstrates the reliability of the proposed method.