Luca Marchesotti

Dual camera system for face detection in unconstrained environments

A system for face detection in outdoor environments for multisensor video surveillance applications is presented. The system is characterized by a combination of two pan-tilt video cameras, which cooperate in order to track and to characterize moving objects with positioning and biometric informations. The final result of the action of the system is the collection of small video shots regarding the face of humans detected in outdoor environments with a robust behavior.

Multiple object tracking under heavy occlusions by using Kalman filters based on shape matching

This paper describes a technique for tracking single objects moving within the guarded scene during dynamic occlusion situations. The processing modules used for object detection and tracking will be shown in detail and the performances of the algorithm discussed. The proposed approach uses an empty reference image for object extraction through image difference; the reference frame is updated continuously by a background updating module taking into account the detected objects. The tracking module is responsible for objects labeling being able to preserve objects identity even when an overlapping occurs on the image plane between different objects. A shape matching technique is used that is based on a linear Kalman filter. The system has been tested on several outdoor sequences showing dynamic occlusions among objects in order to show the validity of the approach.

Structured context-analysis techniques in biologically inspired ambient-intelligence systems

In this paper, techniques and related issues for the definition of a contextual knowledge in ambient-intelligence systems are explored. A logical structure for this kind of system, inspired by a neurobiological brain model, is proposed. Through these considerations, the role and the importance of context awareness in the definition of an artificial organism showing adaptability, pervasiveness, and scalability features are described. Techniques for the definition of a multilayer context representation are explained and practically demonstrated with a test-bed. In the proposed system, a complex event classification is obtained through the fusion of heterogeneous data coming from a set of sensors thanks to the design of a self-organizing map (SOM). The SOM represents the core of the system and testing proofs show good results in the classification of the events taking place in the monitored environment.

A video surveillance architecture for alarm generation and video sequences retrieval

This paper presents a system for automatic video surveillance applications. The system has been designed to monitor outdoor environments such as car parks or streets, providing the human operator with a symbolic description of the scene. The final task of the architecture is to automatically provide alarms when specific events of interest are detected. In this way the level of automation of the system is increased as well as overall performances. One of the main drawbacks of traditional video surveillance systems lies in the alarm generation. This task has to be visually performed by the human operators with intrinsic limitation. The possibility of having this process automated is here described within the design of an architecture capable of acquiring, processing and successfully storing data coming from one or more sensors.

Self-organizing shape description for tracking and classifying multiple interacting objects

Abstract The problem faced in this work is related to tracking and recognition of rigid and non-rigid interacting objects in complex scenes from a static camera. The processing steps leading to the description of the behavior of objects in terms of trajectories and typology will be illustrated in details and the performances of the system will be discussed. The proposed approach uses an empty reference image for object extraction through image difference; the reference frame is updated continuously by a high level background updating module taking into account the detected objects and their classification tag. The tracking module is responsible for objects labelling being able to preserve objects identity even when an occlusion occurs on the image plane between different objects. A novel approach is considered for tracking and recognition, which is based on different features selection strategies applied to an initially redundant set of shape points (i.e. corners). Short-term and long-term memory models are used in a cooperative scheme. The two level feature selection strategy used by long-term shape models is described: at lower level a spatial-temporal voting method is used to assess temporal stability of spatial groups of corners; at the higher level, a supervised self-organizing scheme is used for objects classification.

Cooperative multisensor system for real-time face detection and tracking in uncontrolled conditions

The presented work describes an innovative architecture for multi-sensor distributed video surveillance applications. The aim of the system is to track moving objects in outdoor environments with a cooperative strategy exploiting two video cameras. The system also exhibits the capacity of focusing its attention on the faces of detected pedestrians collecting snapshot frames of face images, by segmenting and tracking them over time at different resolution. The system is designed to employ two video cameras in a cooperative client/server structure: the first camera monitors the entire area of interest and detects the moving objects using change detection techniques. The detected objects are tracked over time and their position is indicated on a map representing the monitored area. The objects’ coordinates are sent to the server sensor in order to point its zooming optics towards the moving object. The second camera tracks the objects at high resolution. As well as the client camera, this sensor is calibrated and the position of the object detected on the image plane reference system is translated in its coordinates referred to the same area map. In the map common reference system, data fusion techniques are applied to achieve a more precise and robust estimation of the objects’ track and to perform face detection and tracking. The work novelties and strength reside in the cooperative multi-sensor approach, in the high resolution long distance tracking and in the automatic collection of biometric data such as a person face clip for recognition purposes.

An agent-based approach for tracking people in indoor complex environments

This paper presents an agent-based architecture designed to functionally combine data from an homogeneous network of sensors for tracking purposes. The system has been developed in a video surveillance context to detect, classify and track moving objects in a scene of interest. Although single camera systems could perform the tasks outlined above, they would not be able to deal with topologically complex environments such as corridor, corners and indoor locations in general. The multi-sensor approach has been used to overcome these problems, nevertheless issues arise such as data fusion, synchronization and camera calibration. The sensor fusion approach here purposed uses autonomous software agents to negotiate the combination of data and the fusion is carried out by appropriate signal processing algorithms. The system has been tested with indoor video sequences to show the systems ability to preserve identity and of correct trajectory estimation of the tracked object.

An Agent Society for Scene Interpretation

The paper presents an agent-based framework for use in scene understanding. The framework is suitable for the development of an intelligent distributed system targeted for smart assistant technologies to aid security staff employed to monitor security areas. The framework was used to implement a car park monitoring prototype system.

VIDEO PROCESSING AND UNDERSTANDING TOOLS FOR AUGMENTED MULTISENSOR PERCEPTION AND MOBILE USER INTERACTION IN SMART SPACES

In this paper, a complete Smart Space architecture and related system prototype are presented. The system is able to analyze situations of interest in a given environment and to produce related contextual information. Experimental results show that video information plays a major role for what concerns both situation perception and personalized contex-aware communications. For this reason, the poropsed multisensor system automatically extracts information from multiple cameras as well as diverse sensors describing environment status. This information is then used to trigger personalized and context-aware video messages adaptively sent to users. A rule-based module is encharged to customize video messages in relation to the user profile, contextual situation and userss terminal. The systems outputs graphically generated video messages consisting of an animated avatar (i.e. Virtual Character) closing the loop on users. Proposed results validate the conceptual schema behind the architecture and the successf...

A multicamera fusion framework for multiple occluding objects tracking in intelligent monitoring and sport viewing applications

The aim of this paper is to present a multi camera system for location estimation inspired to a model inherited from the Data Fusion domain: the Joint Directorate of Laboratories (JDL) model (E. Waltz et al., 1990). The problem specifically faced is the tracking of objects in two complementary applications: intelligent monitoring (video surveillance) and sport viewing (football players tracking), where multiple occluding objects have to be successfully segmented and located using different features such as color, position and dynamics.