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

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 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

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

A Self-trainable System for Moving People Counting by Scene Partitioning

The paper presents an improved method for estimating the number of moving people in a scene for video surveillance applications; the performance is measured on the public database used in the framework of the PETS international competition, and compared, on the same database, with the ones participating to the same contest up to now. The system exhibits a high accuracy, ranking it at the top positions, and revealed to be so fast to make possible its use in real time surveillance applications