Lucio Marcenaro

Distributed architectures and logical-task decomposition in multimedia surveillance systems

In the past few years, the development of complex surveillance systems has captured the interest of both the research and industrial worlds. Strong and challenging requirements of modern society are involved in this problem, which aims to increase safety and security in several application domains such as transport, tourism, home and bank security, military applications, etc. At the same time, fast improvements in microelectronics, telecommunications, and computer science make it necessary to consider new perspectives in this field. The main objective of this paper is to investigate, discuss, and evaluate the impact of distributed processing and new communication techniques on multimedia surveillance systems, which represent the so-called third-generation surveillance systems (3 GSSs). In particular, aspects related to the distribution of intelligence among multiple-processing and wide-bandwidth resources are discussed in detail. It is shown how distribution of intelligence can be obtained by a hierarchical architecture that partitions, in a dynamic way, the main logical processing tasks (i.e., representation, recognition, and communication) performed in a 3 GSS physical architecture made up of intelligent cameras, hubs, and central control rooms. The advantages of this solution are pointed out in terms of 1) increased flexibility and reconfigurability and 2) optimal allocation of available processing and bandwidth resources. Finally, a case study is analyzed that allows one to gain a deeper insight into a distributed surveillance system.

Image stabilization algorithms for video-surveillance applications

An image stabilization algorithm is presented that is specifically oriented toward video-surveillance applications. The proposed approach is based on a novel motion-compensation method that is an adaptation of a well-known image-stabilization algorithm for visualization in video-surveillance applications. In particular, the illustrated methods take into account the specificity of typical video-surveillance applications, where objects moving in a scene often cover a large part of an image thus causing the failure of classic image-stabilization techniques. Evaluation methods for image stabilization algorithms are discussed.

Automatic detection and indexing of video-event shots for surveillance applications

Increased communication capabilities and automatic scene understanding allow human operators to simultaneously monitor multiple environments. Due to the amount of data to be processed in new surveillance systems, the human operator must be helped by automatic processing tools in the work of inspecting video sequences. In this paper, a novel approach allowing layered content-based retrieval of video-event shots referring to potentially interesting situations is presented. Interpretation of events is used for defining new video-event shot detection and indexing criteria. Interesting events refer to potentially dangerous situations: abandoned objects and predefined human events are considered in this paper. Video-event shot detection and indexing capabilities are used for online and offline content-based retrieval of scenes to be detected.

Advanced image-processing tools for counting people in tourist site-monitoring applications

This work aims at demonstrating the usefulness of exploiting novel image-processing tools for moving-object detection and classification in the context of an actual application involving the remote monitoring of a tourist site. The application concerns outdoor people counting for tourist-flow estimation in a constrained environment. The technical problems to be solved are concerned with: (a) the design and implementation of low-complexity background updating and change detection algorithms able to adapt themselves to the time-varying illumination scene conditions, and (b) the integration of real-time pattern-recognition tools in order to distinguish group of persons to be counted from other objects present in the scene. The achieved results have proven that the proposed system makes it possible to obtain reliable people counting in different environmental situations, with an absolute mean error at most equal to 10%.

Performance evaluation of event detection solutions: the CREDS experience

In video surveillance projects, automatic and real-time event detection solutions are required to guarantee an efficient and cost-effective use of the infrastructure. Many solutions have been proposed to automatically detect a variety of events of interest. However, not all solutions and technologies may satisfy all the requirements of the surveillance scenario. For this reason, performance evaluation of existing event detection solutions becomes an important step in the deployment of video surveillance projects. In this paper, we propose a practical approach that aims at minimizing the ground truth generation problem and the expertise required to evaluate and compare the results by introducing specific requirements of specific event detection scenarios. This approach is believed to be applicable for an initial evaluation of candidate solutions to a specific surveillance scenario before more exhaustive tests in an integrated environment. The proposed method is under evaluation in the framework of the challenge of real-time event detection solutions (CREDS).

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.

Automatic detection of dangerous events for underground surveillance

This paper describes automatic video sequences processing techniques for detecting suspect and dangerous situations within public transportations. Proposed surveillance system is able to raise different kind of warnings and alarms on the basis of the particular detected situation. Algorithms used for objects detection and tracking will be described in details and performances will be discussed in relation with alarm conditions that are showed in the sequences that have been made available for this conference. An empty reference image is used for object extraction through image difference. In order to perform background updating a high level module is implemented taking into account the detected objects and their classification tags. The system has been tested on several sequences showing dangerous events due to human behaviors in an underground station.

A fictitious play-based game-theoretical approach to alleviating jamming attacks for cognitive radios

On-the-fly reconfigurability capabilities and learning prospectives of Cognitive Radios inherently bring a set of new security issues. One of them is intelligent radio frequency jamming, where adversary is able to deploy advanced jamming strategies to degrade performance of the communication system. In this paper, we observe the jamming/antijamming problem from a game-theoretical perspective. A game with incomplete information on opponents payoff and strategy is modelled as a Markov Decision Process (MDP). A variant of fictitious play learning algorithm is deployed to find optimal strategies in terms of combination of channel hopping and power alteration anti-jamming schemes.

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.