Publications

This paper proposes a system for people trajectory shape analysis by exploiting a statistical approach which accounts for sequences of both directional (the directions of the trajectory) and linear (the speeds) data. A semi-directional distribution (AWLG - Approximated Wrapped and Linear Gaussian) is used with a mixture to find main directions and speeds. A variational version of the mutual information criterion is proposed to prove the statistical dependency of the data. Then, in order to compare data sequences, we define an inexact method with a Kullback-Leibler-based distance measure and employ a global alignment technique is to handle sequences of different lengths and with local shifts or deformations. A comprehensive analysis of variable dependency and parameter estimation techniques are reported and evaluated on both synthetic and real data sets.

The detection of humans in very complex scenes can be very challenging, due to the performance degradation of classical motion detection and tracking approaches. An alternative approach is the detection of human-like patterns over the whole image. The present paper follows this line by extending Tuzel et al.’s technique [1] based on covariance descriptors and LogitBoost algorithm applied over Riemannian manifolds. Our proposal represents a significant extension of it by: (a) exploiting motion information to focus the attention over areas in which motion is present or was present in the recent past; (b) enriching the human classifier by additional, dedicated cascades trained on positive and negative samples taken from the specific scene; (c) using a rough estimation of the scene perspective, to reduce false detections and improve system performance. This approach is suitable in multi-camera scenarios, since the monolithic block for human-detection remains the same for the whole system, whereas the parameter tuning and set-up of the three proposed extensions (the only camera-dependent parts of the system), are automatically computed for each camera. The approach has been tested on a construction working site in which complexity and dynamics are very high, making human detection a real challenge. The experimental results demonstrate the improvements achieved by the proposed approach.

AD-HOC copes with the problem of multiple people tracking in video surveillance in presence of large occlusions. The main novelty is the adoption of an appearance-based approach in a formal Bayesian framework: the status of each object is defined at pixel level, where each pixel is characterized by the appearance, i.e. the color (integrated along the time) and the likelihood to belong to the object. With these data at pixel-level and a probability of non-occlusion at object-level, the problem of occlusions is addressed. The method does not aim at detecting the presence of an occlusion only, but classifies the type of occlusion at a sub-region level and evolve the status of theobject in a selective way. The AD-HOC tracking has been tested in many application for indoor and outdoor surveillance. Results on PETS2006 test set are reported where many people and abandoned objects are detected and tracked.

Mobile video surveillance represents a new paradigm that encompasses, on the one side, ubiquitous video acquisition and, on the other side, ubiquitous video processing and viewing, addressing both computer-based and human-based surveillance. To this aim, systems must provide efficient video streaming with low latency and low frame skipping, even over limited bandwidth networks. This work presents MoSES (MObile Streaming for vidEo Surveillance), an effective system for mobile video surveillance for both PC and PDA clients; it relies over H.264/AVC video coding and GPRS/EDGE-GPRS network. Adaptive control algorithms are employed to achieve the best tradeoff between low latency and good video fluidity. MoSES provides a good-quality video streaming that is used as input to computer-based video surveillance applications for people segmentation and tracking. In this paper new and general-purpose methodologies for streaming performance evaluation are also proposed and used to compare MoSES with existing solutions in terms of different parameters (latency, image quality, video fluidity, and frame losses), as well as in terms of performance in people segmentation and tracking.

This paper presents a method for recognizing human actions in a multi-camera setup. The proposed method automatically extracts significant points on the human body, without the need of artificial markers. A sophisticated appearance-based tracking able to cope with occlusions is exploited to extract a probability map for each moving object. A segmentation technique based on mixture of Gaussians is then employed to extract and track significant points on this map, corresponding to significant regions on the human silhouette. The point tracking produces a set of 3D trajectories that are compared with other trajectories by means of global alignment and dynamic programming techniques. Preliminary experiments showed the potentiality of the proposed approach.

Outdoor surveillance is one of the most attractive application of video processing and analysis. Robust algorithms must be defined and tuned to cope with the non-idealities of outdoor scenes. For instance, in a public park, an automatic video surveillance system must discriminate between shadows, reflections, waving trees, people standing still or moving, and other objects. Visual knowledge coming from multiple cameras can disambiguate cluttered and occluded targets by providing a continuous consistent labeling of tracked objects among the different views. This work proposes a new approach for coping with this problem in multi-camera systems with overlapped Fields of View (FoVs). The presence of overlapped zones allows the definition of a geometry-based approach to reconstruct correspondences between FoVs, using only homography and epipolar lines (hereinafter HECOL: Homography and Epipolar-based COnsistent Labeling) computed automatically with a training phase. We also propose a complete system that provides segmentation and tracking of people in each camera module. Segmentation is performed by means of the SAKBOT (Statistical and Knowledge Based Object Tracker) approach, suitably modified to cope with multi-modal backgrounds, reflections and other artefacts, typical of outdoor scenes. The extracted objects are tracked using a statistical appearance model robust against occlusions and segmentation errors. The main novelty of this paper is the approach to consistent labeling. A specific Camera Transition Graph is adopted to efficiently select the possible correspondence hypotheses between labels. A Bayesian MAP optimization assigns consistent labels to objects detected by several points of views: the object axis is computed from the shape tracked in each camera module and homography and epipolar lines allow a correct axis warping in other image planes. Both forward and backward probability contributions from the two different warping directions make the approach robust against segmentation errors, and capable of disambiguating groups of people. The system has been tested in a real setup of a urban public park, within the Italian LAICA (Laboratory of Ambient Intelligence for a friendly city) project. The experiments show how the system can correctly track and label objects in a distributed system with real-time performance. Comparisons with simpler consistent labeling methods and extensive outdoor experiments with ground truth demonstrate the accuracy and robustness of the proposed approach.

Smoke detection calls for a reliable and fast distinction between background, moving objects and variable shapes that are recognizable as smoke. In our system we propose a stable background suppression module joined with a smoke detection module working on segmented objects. It exploits two features: the energy variation in wavelet model and a color model of the smoke. The decrease of energy ratio in wavelet domain between background and current image is a clue to detect smoke representing the variations of texture level. A mixture of Gaussians models this texture ratio for temporal evolution. The color model is used as reference to measure the deviation of the current pixel color from the model. The two features have been combined using a Bayesian classifier to detect smoke in the scene. Experiments on real data and a comparison between our background model and Gaussian Mixture(MOG) model for smoke detection are presented. © 2008 IEEE.