ELCVIA Electronic Letters on Computer Vision and Image Analysis, 2003
In this paper, we propose a robust real-time object detection system for outdoor image sequences ... more In this paper, we propose a robust real-time object detection system for outdoor image sequences acquired by an active camera. The system is able to compensate background changes due to the camera motion and to detect mobile objects in the scene. Background compensation is performed by assuming a simple translation (displacement vector) of the background from the previous to the current frame and by applying the well-known tracker proposed by Lucas and Kanade. A reference map containing all well trackable features is maintained and updated by the system at each frame by introducing new good features related to new regions that appear in the current image. A new method is applied to reject badly tracked features. The current frame and the background after compensation are processed by a change detection method in order to locate mobile objects. Results are presented in the contest of a visual-based surveillance system for monitoring outdoor enviroments.
Surveillance systems include a large set of techniques for both low level and high level tasks. I... more Surveillance systems include a large set of techniques for both low level and high level tasks. In particular, in the last decade the research community has witnessed a high proliferation of techniques that span from object detection and tracking to object recognition and event understanding. Although some techniques have been proved to be very effective, those tasks cannot be considered solved. Even less, we can consider concluded the research in the field of the analysis of the activities (event analysis). It is this topic together with the problem of the information sharing among different sensors that represents the core of this work. Here, a system architecture for a video surveillance system with distributed intelligence over multiple processing units and with distributed communication over multiple heterogeneous channels (wireless, satellite, local IP networks, etc.) is proposed. A new real-time technique for changing the video transmission parameters (e.g., frame rate, spatial/colour resolution, etc.) according to the available bandwidth (which depends on the number of the detected alarm situations, on the required video quality, etc.) will be presented.
Pan-tilt-zoom (PTZ) cameras are able to dynamically modify their field of view (FOV). This functi... more Pan-tilt-zoom (PTZ) cameras are able to dynamically modify their field of view (FOV). This functionality introduces new capabilities to camera networks such as increasing the resolution of moving targets and adapting the sensor coverage. On the other hand, PTZ functionality requires solutions to new challenges such as controlling the PTZ parameters, estimating the ego motion of the cameras, and calibrating the moving cameras.This tutorial provides an overview of the main video processing techniques and the currents trends in this active field of research. Autonomous PTZ cameras mainly aim to detect and track targets with the largest possible resolution. Autonomous PTZ operation is activated once the network detects and identifies an object as sensible target and requires accurate control of the PTZ parameters and coordination among the cameras in the network. Therefore, we present cooperative localization and tracking methods, i.e., multiagentand consensus-based approaches to jointly compute the target's properties such as ground-plane position and velocity. Stereo vision exploiting wide baselines can be used to derive three-dimensional (3-D) target localization. This tutorial further presents different techniques for controlling PTZ camera handoff, configuring the network to dynamically track targets, and optimizing the network configuration to increase coverage probability. It also discusses implementation aspects for these video processing techniques on embedded smart cameras, with a special focus on data access properties.
Abstract In this paper, a robust real-time face detection system based on the integration of diff... more Abstract In this paper, a robust real-time face detection system based on the integration of different location methods is proposed. A hierarchical architecture composed of three levels is designed. At the first level, a change detection method is applied to detect blobs of moving objects (ie, humans) in the scene. Then, the silhouette of each blob is analyzed to focalize the attention of the system on small image areas where the probability of finding human heads is high. At the second level, two different methods, ie, the skin color and the ...
In this paper, a new classifier, called adaptive high order neural tree (AHNT), is proposed for p... more In this paper, a new classifier, called adaptive high order neural tree (AHNT), is proposed for pattern recognition applications. It is a hierarchical multi-level neural network, in which the nodes are organized into a tree topology. It successively partitions the training set into subsets, assigning each subset to a different child node. Each node can be a first-order or a high order perceptron (HOP) according to the complexity of the local training set. First order perceptrons split the training set by hyperplanes, while n-order perceptrons use n-dimensional surfaces. An adaptive procedure decides the best order of the HOP to be applied at a given node of the tree. The AHNT is grown automatically during the learning phase: its hybrid structure guarantees a reduction of the number of internal nodes with respect to classical neural trees and reaches a greater generalization capability. Moreover, it overcomes the classical problems of feed-forward neural networks (e.g., multilayer perceptrons) since both types of perceptrons does not require any a-priori information about the number of neurons, hidden layers, or neuron connections. Tests on patterns with different distributions and comparisons with classical neural tree-based classifiers have been performed to demonstrate the validity of the proposed method.
Abstract Many countries around the world have implemented or are in the process of implementing t... more Abstract Many countries around the world have implemented or are in the process of implementing tighter security measures in public and private places. Such measures are becoming widespread and are applied not only at government, military, and corporate facilities, but also in civilian infrastructures. Modern surveillance system consist of different modules. A sensor layer usually consists of a network of cameras, audio arrays, physical perimeter sensors, and other types of information feeders. A surveillance layer represents ...
Abstract Recently the surveillance of wide areas has pointed the interest of the research communi... more Abstract Recently the surveillance of wide areas has pointed the interest of the research community. The use of active vision seems to be the most effective solutions for these needs. Against the better acquiring resolution there is the problem of the apparent motion inducted by the camera motion known as ego-motion. Feature based methods for ego-motion estimation are widely used in computer vision but they deal with feature recovery and with errors in feature tracking. In this paper, we propose a fast method to extract and select new ...
Timely recognition of threats can be significantly supported by security assistance systems that ... more Timely recognition of threats can be significantly supported by security assistance systems that work continuously in time and call the security personnel in case of anomalous events in the surveillance area. We describe the concept and the realization of an indoor security assistance system for real-time decision support. The system consists of a computer vision module and a person classification module. The computer vision module provides a video event analysis of the entrance region in front of the demonstrator. After entering the control corridor, the persons are tracked, classified, and potential threats are localized inside the demonstrator. Data for the person classification are provided by chemical sensors detecting hazardous materials. Due to their limited spatio-temporal resolution, a single chemical sensor cannot localize this material and associate it with a person. We compensate this deficiency by fusing the output of multiple, distributed chemical sensors with kinematical data from laser-range scanners. Considering both the computer vision formation and the results of the person classification affords the localization of threats and a timely reaction of the security personnel.
Feature based methods for ego-motion estimation are widely used in computer vision but they must ... more Feature based methods for ego-motion estimation are widely used in computer vision but they must deal with errors in feature tracking. In this paper, we propose a robust real-time method for ego-motion estimation by assuming an affine motion of the background from the previous to the current frame. A new clustering technique is applied on image's subareas to select in a fast and reliable way three features for the affine transform computation. The previous frame after being warped according to the computed affine transform is processed with the current frame by a change detection method in order to detect mobile objects. Results are presented in the context of a visual-based surveillance system for monitoring outdoor environments.
ELCVIA Electronic Letters on Computer Vision and Image Analysis, 2003
In this paper, we propose a robust real-time object detection system for outdoor image sequences ... more In this paper, we propose a robust real-time object detection system for outdoor image sequences acquired by an active camera. The system is able to compensate background changes due to the camera motion and to detect mobile objects in the scene. Background compensation is performed by assuming a simple translation (displacement vector) of the background from the previous to the current frame and by applying the well-known tracker proposed by Lucas and Kanade. A reference map containing all well trackable features is maintained and updated by the system at each frame by introducing new good features related to new regions that appear in the current image. A new method is applied to reject badly tracked features. The current frame and the background after compensation are processed by a change detection method in order to locate mobile objects. Results are presented in the contest of a visual-based surveillance system for monitoring outdoor enviroments.
Surveillance systems include a large set of techniques for both low level and high level tasks. I... more Surveillance systems include a large set of techniques for both low level and high level tasks. In particular, in the last decade the research community has witnessed a high proliferation of techniques that span from object detection and tracking to object recognition and event understanding. Although some techniques have been proved to be very effective, those tasks cannot be considered solved. Even less, we can consider concluded the research in the field of the analysis of the activities (event analysis). It is this topic together with the problem of the information sharing among different sensors that represents the core of this work. Here, a system architecture for a video surveillance system with distributed intelligence over multiple processing units and with distributed communication over multiple heterogeneous channels (wireless, satellite, local IP networks, etc.) is proposed. A new real-time technique for changing the video transmission parameters (e.g., frame rate, spatial/colour resolution, etc.) according to the available bandwidth (which depends on the number of the detected alarm situations, on the required video quality, etc.) will be presented.
Pan-tilt-zoom (PTZ) cameras are able to dynamically modify their field of view (FOV). This functi... more Pan-tilt-zoom (PTZ) cameras are able to dynamically modify their field of view (FOV). This functionality introduces new capabilities to camera networks such as increasing the resolution of moving targets and adapting the sensor coverage. On the other hand, PTZ functionality requires solutions to new challenges such as controlling the PTZ parameters, estimating the ego motion of the cameras, and calibrating the moving cameras.This tutorial provides an overview of the main video processing techniques and the currents trends in this active field of research. Autonomous PTZ cameras mainly aim to detect and track targets with the largest possible resolution. Autonomous PTZ operation is activated once the network detects and identifies an object as sensible target and requires accurate control of the PTZ parameters and coordination among the cameras in the network. Therefore, we present cooperative localization and tracking methods, i.e., multiagentand consensus-based approaches to jointly compute the target's properties such as ground-plane position and velocity. Stereo vision exploiting wide baselines can be used to derive three-dimensional (3-D) target localization. This tutorial further presents different techniques for controlling PTZ camera handoff, configuring the network to dynamically track targets, and optimizing the network configuration to increase coverage probability. It also discusses implementation aspects for these video processing techniques on embedded smart cameras, with a special focus on data access properties.
Abstract In this paper, a robust real-time face detection system based on the integration of diff... more Abstract In this paper, a robust real-time face detection system based on the integration of different location methods is proposed. A hierarchical architecture composed of three levels is designed. At the first level, a change detection method is applied to detect blobs of moving objects (ie, humans) in the scene. Then, the silhouette of each blob is analyzed to focalize the attention of the system on small image areas where the probability of finding human heads is high. At the second level, two different methods, ie, the skin color and the ...
In this paper, a new classifier, called adaptive high order neural tree (AHNT), is proposed for p... more In this paper, a new classifier, called adaptive high order neural tree (AHNT), is proposed for pattern recognition applications. It is a hierarchical multi-level neural network, in which the nodes are organized into a tree topology. It successively partitions the training set into subsets, assigning each subset to a different child node. Each node can be a first-order or a high order perceptron (HOP) according to the complexity of the local training set. First order perceptrons split the training set by hyperplanes, while n-order perceptrons use n-dimensional surfaces. An adaptive procedure decides the best order of the HOP to be applied at a given node of the tree. The AHNT is grown automatically during the learning phase: its hybrid structure guarantees a reduction of the number of internal nodes with respect to classical neural trees and reaches a greater generalization capability. Moreover, it overcomes the classical problems of feed-forward neural networks (e.g., multilayer perceptrons) since both types of perceptrons does not require any a-priori information about the number of neurons, hidden layers, or neuron connections. Tests on patterns with different distributions and comparisons with classical neural tree-based classifiers have been performed to demonstrate the validity of the proposed method.
Abstract Many countries around the world have implemented or are in the process of implementing t... more Abstract Many countries around the world have implemented or are in the process of implementing tighter security measures in public and private places. Such measures are becoming widespread and are applied not only at government, military, and corporate facilities, but also in civilian infrastructures. Modern surveillance system consist of different modules. A sensor layer usually consists of a network of cameras, audio arrays, physical perimeter sensors, and other types of information feeders. A surveillance layer represents ...
Abstract Recently the surveillance of wide areas has pointed the interest of the research communi... more Abstract Recently the surveillance of wide areas has pointed the interest of the research community. The use of active vision seems to be the most effective solutions for these needs. Against the better acquiring resolution there is the problem of the apparent motion inducted by the camera motion known as ego-motion. Feature based methods for ego-motion estimation are widely used in computer vision but they deal with feature recovery and with errors in feature tracking. In this paper, we propose a fast method to extract and select new ...
Timely recognition of threats can be significantly supported by security assistance systems that ... more Timely recognition of threats can be significantly supported by security assistance systems that work continuously in time and call the security personnel in case of anomalous events in the surveillance area. We describe the concept and the realization of an indoor security assistance system for real-time decision support. The system consists of a computer vision module and a person classification module. The computer vision module provides a video event analysis of the entrance region in front of the demonstrator. After entering the control corridor, the persons are tracked, classified, and potential threats are localized inside the demonstrator. Data for the person classification are provided by chemical sensors detecting hazardous materials. Due to their limited spatio-temporal resolution, a single chemical sensor cannot localize this material and associate it with a person. We compensate this deficiency by fusing the output of multiple, distributed chemical sensors with kinematical data from laser-range scanners. Considering both the computer vision formation and the results of the person classification affords the localization of threats and a timely reaction of the security personnel.
Feature based methods for ego-motion estimation are widely used in computer vision but they must ... more Feature based methods for ego-motion estimation are widely used in computer vision but they must deal with errors in feature tracking. In this paper, we propose a robust real-time method for ego-motion estimation by assuming an affine motion of the background from the previous to the current frame. A new clustering technique is applied on image's subareas to select in a fast and reliable way three features for the affine transform computation. The previous frame after being warped according to the computed affine transform is processed with the current frame by a change detection method in order to detect mobile objects. Results are presented in the context of a visual-based surveillance system for monitoring outdoor environments.
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Papers by Christian Micheloni