Lauro Snidaro

Active video-based surveillance system: the low-level image and video processing techniques needed for implementation

The importance of video surveillance techniques has considerably increased since the latest terrorist incidents. Safety and security have become critical in many public areas, and there is a specific need to enable human operators to remotely monitor the activity across large environments. For these reasons, multicamera systems are needed to provide surveillance coverage across a wide area, ensuring object visibility over a large range of depths. In the development of advanced visual-based surveillance systems, a number of key issues critical to its successful operation must be addressed. This article describes the low-level image and video processing techniques needed to implement a modern surveillance system. In particular, the change detection methods for both fixed and mobile cameras (pan and tilt) are introduced and the registration methods for multicamera systems with overlapping and nonoverlapping views are discussed.

Trajectory clustering and its applications for video surveillance

In this paper we present a trajectory clustering method suited for video surveillance and monitoring systems. The clusters are dynamic and built in real-time as the trajectory data is acquired, without the need of an off-line processing step. We show how the obtained clusters can be successfully used both to give proper feedback to the low-level tracking system and to collect valuable information for the high-level event analysis modules.

Video security for ambient intelligence

Moving toward the implementation of the intelligent building idea in the framework of ambient intelligence, a video security application for people detection, tracking, and counting in indoor environments is presented in this paper. In addition to security purposes, the system may be employed to estimate the number of accesses in public buildings, as well as the preferred followed routes. Computer vision techniques are used to analyze and process video streams acquired from multiple video cameras. Image segmentation is performed to detect moving regions and to calculate the number of people in the scene. Testing was performed on indoor video sequences with different illumination conditions.

Real-time thresholding with Euler numbers

The problem of finding an automatic thresholding technique is well known in applications involving image differencing like visual-based surveillance systems, autonomous vehicle driving, etc. Among the algorithms proposed in the past years, the thresholding technique based on the stable Euler number method is considered one of the most promising in terms of visual results. Unfortunately its high computational complexity made it an impossible choice for real-time applications. The implementation here proposed, called fast Euler numbers, overcomes the problem since it calculates all the Euler numbers in just one single raster scan of the image. That is, it runs in O(hw), where h and w are the images height and width, respectively. A technique for determining the optimal threshold, called zero crossing, is also proposed.

Quality-Based Fusion of Multiple Video Sensors for Video Surveillance

In this correspondence, we address the problem of fusing data for object tracking for video surveillance. The fusion process is dynamically regulated to take into account the performance of the sensors in detecting and tracking the targets. This is performed through a function that adjusts the measurement error covariance associated with the position information of each target according to the quality of its segmentation. In this manner, localization errors due to incorrect segmentation of the blobs are reduced thus improving tracking accuracy. Experimental results on video sequences of outdoor environments show the effectiveness of the proposed approach.

Fusing uncertain knowledge and evidence for maritime situational awareness via Markov Logic Networks

The concepts of event and anomaly are important building blocks for developing a situational picture of the observed environment. We here relate these concepts to the JDL fusion model and demonstrate the power of Markov Logic Networks (MLNs) for encoding uncertain knowledge and compute inferences according to observed evidence. MLNs combine the expressive power of first-order logic and the probabilistic uncertainty management of Markov networks. Within this framework, different types of knowledge (e.g. a priori, contextual) with associated uncertainty can be fused together for situation assessment by expressing unobservable complex events as a logical combination of simpler evidences. We also develop a mechanism to evaluate the level of completion of complex events and show how, along with event probability, it could provide additional useful information to the operator. Examples are demonstrated on two maritime scenarios of rules for event and anomaly detection.

A distributed sensor network for video surveillance of outdoor environments

A distributed sensor network (DSN) for video surveillance is presented. The system is able to manage heterogeneous sensors (e.g. optical, infrared, radar, etc.) to operate during night and day and in the presence of different weather conditions (e.g. fog, rain, etc.). Data fusion is therefore mandatory and exploited at different levels to integrate the information produced by the sensors. The architecture presented has low network requirements, is easily scalable, maintainable and allows an easy distribution of the system on a wide outdoor area.

Event classification for automatic visual-based surveillance of parking lots

In this paper, a visual-based surveillance system for real-time event detection and classification in parking lots is presented. The focus is on the high-level part of the system, i.e., the event recognition (ER) module, which is able to analyze two kinds of events (i.e., simple and composite events) that occur in the observed scene. Simple events are represented by single moving objects, e.g., vehicles, pedestrians, etc., while a composite event is represented by a set of temporally consecutive simple events, e.g., people exiting a car just entered in the parking area. An adaptive high order neural tree (AHNT) is applied for recognizing both objects and complex events.

Overview of Contextual Tracking approaches in Information Fusion

Many information fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of: technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this paper, we seek to define and categorize different types of contextual information. We describe five contextual information categories that support target tracking: (1) domain knowledge from a user to aid the information fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road information for target tracking and identification. Appropriate characterization and representation of contextual information is needed for future high-level information fusion systems design to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application.

Representing and recognizing complex events in surveillance applications

In this paper, we investigate the problem of representing and maintaining rule knowledge for a video surveillance application. We focus on complex events representation which cannot be straightforwardly represented by canonical means. In particular, we highlight the ongoing efforts for a unifying framework for computable rule and taxonomical knowledge representation.