Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010
In order to perform automatic analysis of sport videos acquired from a multi-sensing environment,... more In order to perform automatic analysis of sport videos acquired from a multi-sensing environment, it is fundamental to face the problem of automatic football team discrimination. A correct assignment of each player to the relative team is a preliminary task that together with player detection and tracking algorithms can strongly affect any high level semantic analysis. Supervised approaches for object
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009
ABSTRACT Visual surveillance is often based on background subtraction; it usually detects moving ... more ABSTRACT Visual surveillance is often based on background subtraction; it usually detects moving regions in a rough way, with the presence of shadows, ghosts and reflections. In order to improve quality of segmented objects by removing these artifacts in this work we propose an approach based on edge matching. The basic idea is that edges extracted in shadow (or ghost) regions in current image exactly match with edges extracted in the same regions in the background image. On the contrary, edges extracted on foreground objects have not correspondent edges in the background image. A preliminary segmentation procedure based on the uniformity of photometric gain between adjacent points has been carried out to allow a better shadow removing. The algorithm has been tested in many different real contexts, both in indoor and outdoor context.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010
ABSTRACT Automatic sport team discrimination, that is the correct assignment of each player to th... more ABSTRACT Automatic sport team discrimination, that is the correct assignment of each player to the relative team, is a fundamental step in high level sport video sequences analysis applications. In this work we propose a novel set of features based on a variation of classic color histograms called Positional Histograms: these features try to overcome the main drawbacks of classic histograms, first of all the weakness of any kind of relation between spectral and spatial contents of the image. The basic idea is to extract histograms as a function of the position of points in the image, with the goal of maintaining a relationship between the color distribution and the position: this is necessary because often the actors in a play field dress in a similar way, with just a different distribution of the same colors across the silhouettes. Further, different unsupervised classifiers and different feature sets are jointly evaluated with the goal of investigate toward the feasibility of unsupervised techniques in sport video analysis.
ABSTRACT The correct assessment of the condition of a railroad requires the consideration of diff... more ABSTRACT The correct assessment of the condition of a railroad requires the consideration of different factors. Some factors, such as the condition of the ties, can be measured by inspecting features visible from the surface of the railway. Other factors include the condition of the ballast; it is important to recognize the critical situation in which any foreign object can be present on the ballast. These kinds of objects could be cans, pieces of sheet and everything over a well determined dimension. Extensive human resources are currently applied to the problem of evaluating railroad health. The proposed visual inspection system uses images acquired from a digital line scan camera installed under a train. Here we focus on the problem of foreign object detection in the railway maintenance context. To obtain this aim we train a Multilayer Perceptron Network (MLPN) with the edge histogram of the ballast patches manually extracted from the acquired digital image sequence. The general performances of the system, in terms of speed and detection rate, are mainly influenced by the adopted features for representing images and by their number. By this inspection system it is possible to aid the personnel in railway safety issues because a high detection rate percentage has been obtained. We show the adopted techniques by using images acquired in real experimental conditions. Keywords: obstacle detection, ballast inspection, neural networks. 1 Introduction Inspection of the rail state is one of the basic tasks in railway maintenance. In the last few years a large number of methods have been proposed by the computer vision community for facing the problem of visual inspection [1, 2]. The
Seventh International Workshop on Computer Architecture for Machine Perception (CAMP'05), 2005
Rail inspection is a very important task in railway maintenance and it is periodically needed for... more Rail inspection is a very important task in railway maintenance and it is periodically needed for preventing dangerous situations. Inspection is operated manually by trained human operator walking along the track searching for visual anomalies. This monitoring is unacceptable for slowness and lack of objectivity, because the results are related to the ability of the observer to recognize critical situations.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010
In order to perform automatic analysis of sport videos acquired from a multi-sensing environment,... more In order to perform automatic analysis of sport videos acquired from a multi-sensing environment, it is fundamental to face the problem of automatic football team discrimination. A correct assignment of each player to the relative team is a preliminary task that together with player detection and tracking algorithms can strongly affect any high level semantic analysis. Supervised approaches for object
2008 2nd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2008, 2008
ABSTRACT In the last decade, several research efforts have been undertaken in soccer video analys... more ABSTRACT In the last decade, several research efforts have been undertaken in soccer video analysis. This increasing interest is motivated by the possible applications over a wide spectrum of topics: indexing, summarization, video enhancement, team and players statistics, tactic analysis, refereepsilas support, etc. Soccer video analysis requires different challenging tasks: ball and players have to be localized in each frame, tracked over time and, above all, their interactions (passes/shoots) have to be detected and analyzed. The latter task is fundamental, especially for statistics and referee decision support purposes, but, unfortunately, it has not received adequate attention from the scientific community. In this paper a multi-view system able to understand in real time the interactions between the ball and the players is presented. The 3D ball trajectories are, firstly, extracted by triangulation from multiple fixed cameras and then projected on a virtual play-field where they are temporally analyzed to detect their variations generated by the interaction with the players. Inference processes are then introduced to fix the instant of the detected interaction and, finally, the player kicking the ball is identified by analyzing human body configuration with an innovative neural approach based on a Contourlet representation of human silhouette data. The system has been tested during several matches of the Italian first division football championship and experimental proofs of its effectiveness are reported.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009
ABSTRACT Motion detection algorithms usually detect moving regions in a rough way; in some applic... more ABSTRACT Motion detection algorithms usually detect moving regions in a rough way; in some application contexts it could be mandatory to obtain the exact shape of such objects by removing cast shadows as well as ghosts and reflections due to variations in light conditions. To address this problem we propose an approach based on edge matching. The basic idea is that edges extracted in shadow (or ghost) regions in current image exactly match with edges extracted in the correspondent regions in the background image. On the contrary, edges extracted on foreground objects have not correspondent edges in the background image. In order to remove all shadow regions instead of only shadow points, we firstly segment the foreground image into subregions, according to uniformity of photometric gain between adjacent points. The algorithm has been tested in many different real contexts, both in indoor and outdoor environments.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009
ABSTRACT Human action recognition is an important research area in the field of computer vision h... more ABSTRACT Human action recognition is an important research area in the field of computer vision having a great number of real-world applications. This paper presents a multi-view action recognition framework able to extract human silhouette clues from different synchronized static cameras and then to validate them by analyzing scene dynamics. Two different algorithmic procedures were introduced: the first one performs, in each acquired image, the neural recognition of the human body configuration by using a novel mathematical tool called Contourlet transform. The second procedure performs, instead, 3D ball and player motion analysis. The outcomes of both procedures are then merged to accomplish the final player action recognition task. Experiments were carried out on several image sequences acquired during some matches of the Italian "Serie A" soccer championship.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010
In order to perform automatic analysis of sport videos acquired from a multi-sensing environment,... more In order to perform automatic analysis of sport videos acquired from a multi-sensing environment, it is fundamental to face the problem of automatic football team discrimination. A correct assignment of each player to the relative team is a preliminary task that together with player detection and tracking algorithms can strongly affect any high level semantic analysis. Supervised approaches for object
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009
ABSTRACT Visual surveillance is often based on background subtraction; it usually detects moving ... more ABSTRACT Visual surveillance is often based on background subtraction; it usually detects moving regions in a rough way, with the presence of shadows, ghosts and reflections. In order to improve quality of segmented objects by removing these artifacts in this work we propose an approach based on edge matching. The basic idea is that edges extracted in shadow (or ghost) regions in current image exactly match with edges extracted in the same regions in the background image. On the contrary, edges extracted on foreground objects have not correspondent edges in the background image. A preliminary segmentation procedure based on the uniformity of photometric gain between adjacent points has been carried out to allow a better shadow removing. The algorithm has been tested in many different real contexts, both in indoor and outdoor context.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010
ABSTRACT Automatic sport team discrimination, that is the correct assignment of each player to th... more ABSTRACT Automatic sport team discrimination, that is the correct assignment of each player to the relative team, is a fundamental step in high level sport video sequences analysis applications. In this work we propose a novel set of features based on a variation of classic color histograms called Positional Histograms: these features try to overcome the main drawbacks of classic histograms, first of all the weakness of any kind of relation between spectral and spatial contents of the image. The basic idea is to extract histograms as a function of the position of points in the image, with the goal of maintaining a relationship between the color distribution and the position: this is necessary because often the actors in a play field dress in a similar way, with just a different distribution of the same colors across the silhouettes. Further, different unsupervised classifiers and different feature sets are jointly evaluated with the goal of investigate toward the feasibility of unsupervised techniques in sport video analysis.
ABSTRACT The correct assessment of the condition of a railroad requires the consideration of diff... more ABSTRACT The correct assessment of the condition of a railroad requires the consideration of different factors. Some factors, such as the condition of the ties, can be measured by inspecting features visible from the surface of the railway. Other factors include the condition of the ballast; it is important to recognize the critical situation in which any foreign object can be present on the ballast. These kinds of objects could be cans, pieces of sheet and everything over a well determined dimension. Extensive human resources are currently applied to the problem of evaluating railroad health. The proposed visual inspection system uses images acquired from a digital line scan camera installed under a train. Here we focus on the problem of foreign object detection in the railway maintenance context. To obtain this aim we train a Multilayer Perceptron Network (MLPN) with the edge histogram of the ballast patches manually extracted from the acquired digital image sequence. The general performances of the system, in terms of speed and detection rate, are mainly influenced by the adopted features for representing images and by their number. By this inspection system it is possible to aid the personnel in railway safety issues because a high detection rate percentage has been obtained. We show the adopted techniques by using images acquired in real experimental conditions. Keywords: obstacle detection, ballast inspection, neural networks. 1 Introduction Inspection of the rail state is one of the basic tasks in railway maintenance. In the last few years a large number of methods have been proposed by the computer vision community for facing the problem of visual inspection [1, 2]. The
Seventh International Workshop on Computer Architecture for Machine Perception (CAMP'05), 2005
Rail inspection is a very important task in railway maintenance and it is periodically needed for... more Rail inspection is a very important task in railway maintenance and it is periodically needed for preventing dangerous situations. Inspection is operated manually by trained human operator walking along the track searching for visual anomalies. This monitoring is unacceptable for slowness and lack of objectivity, because the results are related to the ability of the observer to recognize critical situations.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010
In order to perform automatic analysis of sport videos acquired from a multi-sensing environment,... more In order to perform automatic analysis of sport videos acquired from a multi-sensing environment, it is fundamental to face the problem of automatic football team discrimination. A correct assignment of each player to the relative team is a preliminary task that together with player detection and tracking algorithms can strongly affect any high level semantic analysis. Supervised approaches for object
2008 2nd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2008, 2008
ABSTRACT In the last decade, several research efforts have been undertaken in soccer video analys... more ABSTRACT In the last decade, several research efforts have been undertaken in soccer video analysis. This increasing interest is motivated by the possible applications over a wide spectrum of topics: indexing, summarization, video enhancement, team and players statistics, tactic analysis, refereepsilas support, etc. Soccer video analysis requires different challenging tasks: ball and players have to be localized in each frame, tracked over time and, above all, their interactions (passes/shoots) have to be detected and analyzed. The latter task is fundamental, especially for statistics and referee decision support purposes, but, unfortunately, it has not received adequate attention from the scientific community. In this paper a multi-view system able to understand in real time the interactions between the ball and the players is presented. The 3D ball trajectories are, firstly, extracted by triangulation from multiple fixed cameras and then projected on a virtual play-field where they are temporally analyzed to detect their variations generated by the interaction with the players. Inference processes are then introduced to fix the instant of the detected interaction and, finally, the player kicking the ball is identified by analyzing human body configuration with an innovative neural approach based on a Contourlet representation of human silhouette data. The system has been tested during several matches of the Italian first division football championship and experimental proofs of its effectiveness are reported.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009
ABSTRACT Motion detection algorithms usually detect moving regions in a rough way; in some applic... more ABSTRACT Motion detection algorithms usually detect moving regions in a rough way; in some application contexts it could be mandatory to obtain the exact shape of such objects by removing cast shadows as well as ghosts and reflections due to variations in light conditions. To address this problem we propose an approach based on edge matching. The basic idea is that edges extracted in shadow (or ghost) regions in current image exactly match with edges extracted in the correspondent regions in the background image. On the contrary, edges extracted on foreground objects have not correspondent edges in the background image. In order to remove all shadow regions instead of only shadow points, we firstly segment the foreground image into subregions, according to uniformity of photometric gain between adjacent points. The algorithm has been tested in many different real contexts, both in indoor and outdoor environments.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009
ABSTRACT Human action recognition is an important research area in the field of computer vision h... more ABSTRACT Human action recognition is an important research area in the field of computer vision having a great number of real-world applications. This paper presents a multi-view action recognition framework able to extract human silhouette clues from different synchronized static cameras and then to validate them by analyzing scene dynamics. Two different algorithmic procedures were introduced: the first one performs, in each acquired image, the neural recognition of the human body configuration by using a novel mathematical tool called Contourlet transform. The second procedure performs, instead, 3D ball and player motion analysis. The outcomes of both procedures are then merged to accomplish the final player action recognition task. Experiments were carried out on several image sequences acquired during some matches of the Italian "Serie A" soccer championship.
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