Francesco Tufano

A Method for Counting People in Crowded Scenes

This paper presents a novel method to count people forvideo surveillance applications. Methods in the literatureeither follow a direct approach, by first detecting people andthen counting them, or an indirect approach, by establishinga relation between some easily detectable scene featuresand the estimated number of people. The indirect approachis considerably more robust, but it is not easy to take intoaccount such factors as perspective or people groups withdifferent densities.The proposed technique, while based on the indirect approach,specifically addresses these problems; furthermoreit is based on a trainable estimator that does not requirean explicit formulation of a priori knowledge about the perspectiveand density effects present in the scene at hand.In the experimental evaluation, the method has beenextensively compared with the algorithm by Albiol et al.,which provided the highest performance at the PETS 2009contest on people counting. The experimentation has usedthe public PETS 2009 datasets. The results confirm that theproposed method improves the accuracy, while retaining therobustness of the indirect approach.

A method for counting moving people in video surveillance videos

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 -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 Albiols algorithm.

Counting Moving People in Videos by Salient Points Detection

This paper presents a novel method to count people for video surveillance applications. The problem is faced by establishing a mapping between some scene features and the number of people. Moreover, the proposed technique takes specifically into account problems due to perspective. In the experimental evaluation, the method has been compared with respect to the algorithm by Albiol et al., which provided the highest performance at the PETS 2009 contest on people counting, using the same datasets. The results confirm that the proposed method improves the accuracy, while retaining the robustness of Albiols algorithm.

An experimental evaluation of foreground detection algorithms in real scenes

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.

Meeting the application requirements of intelligent video surveillance systems in moving object detection

In a video surveillance system, moving object detection is the most challenging problem especially if the system is applied to complex environments with variable lighting, dynamic and articulate scenes, etc. Furthermore, a video surveillance system is a real-time application, so discouraging the use of good, but computationally expensive, solutions. This paper presents a set of improvements of a basic background subtraction algorithm that are suitable for video surveillance applications. Besides we present a new performance evaluation scheme never used in the context of moving object detection algorithms.

Evaluation and improvements of a real-time background subtraction method

In a video surveillance system, moving object detection is the most challenging problem especially if the system is applied in complex environments with variable lighting, dynamic and articulate scenes, etc.. Furthermore, a video surveillance system is a real-time application, so discouraging the use of good, but computationally expensive, solutions. This paper presents a set of improvements of a basic background subtraction algorithm that are suitable for video surveillance applications. Besides we present a new evaluation scheme never used in the context of moving object detection algorithms.

A graph-based method for detecting and classifying clusters in mammographic images

In this paper we propose a method based on a graph-theoretical cluster analysis for automatically finding and classifying clusters of microcalcifications in mammographic images, starting from the output of a microcalcification detection phase. This method does not require the user to provide either the expected number of clusters or any threshold values, often with no clear physical meaning, as other algorithms do. The proposed approach has been tested on a standard database of 40 mammographic images and has demonstrated to be very effective, even when the detection phase gives rise to several false positives.

An algorithm for recovering camouflage errors on moving people

In this paper we present a model-based algorithm working as a post-processing phase of any foreground object detector. The model is suited to recover camouflage errors producing the segmentation of an entity in small and unconnected parts. The model does not require training procedures, but only information about the estimated size of the person, obtainable when an inverse perspective mapping procedure is used. A quantitative evaluation of the effectiveness of the method, used after four well known moving object detection algorithms has been carried out. Performance are given on a variety of publicly available databases, selected among those presenting highly camouflaged objects in real scenes referring to both indoor and outdoor environments.

An Algorithm for Detection of Partially Camouflaged People

Several video analysis applications perform object detection using a background subtraction approach. Camouflage can be a serious problem for these applications, since the objects of interest may appear fragmented into small,disconnected pieces, with a dramatic negative impact on later processing phases such as classification or tracking. Nevertheless, this problem is largely underestimated in the literature. In this paper an effective, model-based solution is presented for the case of people detection. The proposed method acts as a post-processing phase, grouping together the fragmented blocks to restore the original object. A quantitative evaluation of the effectiveness of this method has been performed on real world videos from a video-surveillance application. The videos used for theexperiments (with metadata) have been made publicly available on the Internet.

Reflection removal for people detection in video surveillance applications

In this paper we present a method removing reflection of people on shiny floors in the context of people detection for video analysis applications. The method exploits chromatic properties of the reflections and does not require a geometric model of the objects. An experimental evaluation of the proposed method, performed on a significant database containing several publicly available videos, demonstrates its effectiveness. The proposed technique also favorably compares with respect to other state of the art algorithms for reflection removal.