A Graph-Kernel Method for Re-identification

Re-identification, that is recognizing that an object appearing in a scene is a reoccurrence of an object seen previously by the system (by the same camera or possibly by a different one) is a challenging problem in video surveillance. In this paper, the problem is addressed using a structural, graph-based representation of the objects of interest. A recently proposed graph kernel is adopted for extending to this representation the Principal Component Analyisis (PCA) technique. An experimental evaluation of the method has been performed on two video sequences from the publicly available PETS2009 database

An Experimental Evaluation of Foreground Detection Algorithms in Real Scenes

International audience; Foreground detection is an important preliminary step of many video analysis systems. Many algorithms have been proposed in the last years, but there is not yet a consensus on which approach is the most effective, not even limiting the problem to a single category of videos. This paper aims at constituting a first step towards a reliable assessment of the most commonly used approaches. In particular, four notable algorithms that perform foreground detection have been evaluated using quantitative measures to assess their relative merits and demerits. The evaluation has been carried out using a large, publicly available dataset composed by videos representing different realistic applicative scenarios. The obtained performance is presented and discussed, highlighting the conditions under which algorithm can represent the most effective solution

A Method for Counting People in Crowded Scenes

This paper presents a novel method to count people for video surveillance applications. Methods in the literature either follow a direct approach, by first detecting people and then counting them, or an indirect approach, by establishing a relation between some easily detectable scene features and the estimated number of people. The indirect approach is considerably more robust, but it is not easy to take into account such factors as perspective or people groups with different densities. The proposed technique, while based on the indirect approach, specifically addresses these problems; furthermore it is based on a trainable estimator that does not require an explicit formulation of a priori knowledge about the perspective and density effects present in the scene at hand. In the experimental evaluation, the method has been extensively compared with the algorithm by Albiol et al., which provided the highest performance at the PETS 2009 contest on people counting. The experimentation has used the public PETS 2009 datasets. The results confirm that the proposed method improves the accuracy, while retaining the robustness of the indirect approach

Audio Surveillance of Roads:A System for Detecting Anomalous Sounds

In the last decades, several systems based on video analysis have been proposed for automatically detecting accidents on roads to ensure a quick intervention of emergency teams. However, in some situations, the visual information is not sufficient or sufficiently reliable, whereas the use of microphones and audio event detectors can significantly improve the overall reliability of surveillance systems. In this paper, we propose a novel method for detecting road accidents by analyzing audio streams to identify hazardous situations such as tire skidding and car crashes. Our method is based on a two-layer representation of an audio stream: at a low level, the system extracts a set of features that is able to capture the discriminant properties of the events of interest, and at a high level, a representation based on a bag-of-words approach is then exploited in order to detect both short and sustained events. The deployment architecture for using the system in real environments is discussed, together with an experimental analysis carried out on a data set made publicly available for benchmarking purposes. The obtained results confirm the effectiveness of the proposed approach.</p

An ensemble of rejecting classifiers for anomaly detection of audio events

Audio analytic systems are receiving an increasing interest in the scientific community, not only as stand alone systems for the automatic detection of abnormal events by the interpretation of the audio track, but also in conjunction with video analytics tools for enforcing the evidence of anomaly detection. In this paper we present an automatic recognizer of a set of abnormal audio events that works by extracting suitable features from the signals obtained by microphones installed into a surveilled area, and by classifying them using two classifiers that operate at different time resolutions. An original aspect of the proposed system is the estimation of the reliability of each response of the individual classifiers. In this way, each classifier is able to reject the samples having an overall reliability below a threshold. This approach allows our system to combine only reliable decisions, so increasing the overall performance of the method. The system has been tested on a large dataset of samples acquired from real world scenarios; the audio classes of interests are represented by gunshot, scream and glass breaking in addition to the background sounds. The preliminary results obtained encourage further research in this direction

A Method for Counting Moving People in Video Surveillance Videos

International audience; People counting is an important problem in video surveillance applications. This problem has been faced either by trying to detect people in the scene and then counting them or by establishing a mapping between some scene feature and the number of people (avoiding the complex detection problem). This paper presents a novel method, following this second approach, that is based on the use of SURF features and of an https://static-content.springer.com/image/art%3A10.1155%2F2010%2F231240/MediaObjects/13634_2009_Article_2711_IEq1_HTML.gif -SVR regressor provide an estimate of this count. The algorithm takes specifically into account problems due to partial occlusions and to perspective. In the experimental evaluation, the proposed method has been compared with the algorithm by Albiol et al., winner of the PETS 2009 contest on people counting, using the same PETS 2009 database. The provided results confirm that the proposed method yields an improved accuracy, while retaining the robustness of Albiol's algorithm

Effect of Nanosized TiO2 on Nucleation and Growth of Cristobalite in Sintered Fused Silica Cores for Investment Casting

Sintered fused silica is often used for making sacrificial cores in investment castings of Ni superalloys. Their usage is fundamental in the manufacture of precise superalloy gas turbine components with complex internal cooling passages. In this study SiO2/ZrSiO4/TiO2 cores were prepared from fused silica powders with different grain size and zircon and TiO2 content by slip casting method. Green samples were sintered at 1230°C at various soaking time: from 0,5 to 10 hours. Thermomechanical and microstructural properties of optimized silica obtained by add of 1,5%wt of TiO2 to SiO2/ZrSiO4 composition have been investigated by three point bending tests, XRD and Hg porosimetric analysis. The influence of cristobalite content on thermal stability at high temperature was studied by an optical dilatometer. At temperature below 1200°C TiO2 appears to act as a phase transformation inhibitor reducing the transformation rate of fused silica to cristobalite at high temperatures. At higher temperature it speeds up the formation of cristobalite. A comparison with commercial silica cores made by injection moulding has been performed. A prototype core was obtained and an investment casting was performed on that

A New Approach for Stereo Matching in Autonomous Mobile Robot Applications

We propose a new approach for stereo matching in Autonomous Mobile Robot applications. In this framework an accurate but slow reconstruction of the 3D scene is not needed; rather, it is more important to have a fast localization of the obstacles to avoid them. All the methods in the literature are based on a punctual correspondence, but they are inefficient in realistic contexts for the presence of uniform patterns, or some perturbations between the two images of the stereo pair. Our idea is to face the stereo matching problem as a matching between homologous regions, instead of a point matching. The stereo images are represented as graphs and a graph matching is computed to find homologous regions. We present some results on a standard stereo database and also on a more realistic stereo sequence acquired from a robot moving in an indoor environment, and a performance comparison with other approaches in the literature is reported and discussed. Our method is strongly robust in case of some fluctuations of the stereo pair, homogeneous and repetitive regions, and is fast. The result is a semi-dense disparity map, leaving only a few regions in the scene unmatched.