Vassilios Morellas

Urban surveillance systems: from the laboratory to the commercial world

Research in the surveillance domain was confined for years in the military domain. Recently, as military spending for this kind of research was reduced and the technology matured, the attention of the research and development community turned to commercial applications of surveillance. In this paper we describe a state-of-the-art monitoring system developed by a corporate R&D lab in cooperation with the corresponding security business units. It represents a sizable effort to transfer some of the best results produced by computer vision research into a viable commercial product. Our description spans both practical and technical issues. From the practical point of view we analyze the state of the commercial security market, typical cultural differences between the research team and the business team and the perspective of the potential users of the technology. These are important issues that have to be dealt with or the surveillance technology will remain in the lab for a long time. From the technical point of view we analyze our algorithmic and implementation choices. We describe the improvements we introduced to the original algorithms reported in the literature in response to some problems that arose during field testing. We also provide extensive experimental results that highlight the strong points and some weaknesses of the prototype system.

A vehicle occupant counting system based on near-infrared phenomenology and fuzzy neural classification

We undertook a study to determine if the automatic detection and counting of vehicle occupants is feasible. In the present paper, we report our findings regarding the appropriate sensor phenomenology and arrangement for the task. We propose a novel system based on fusion of near-infrared imaging signals and demonstrate its adequacy with theoretical and experimental arguments. We also propose a fuzzy neural network classifier to operate upon the fused near-infrared imagery and perform the occupant detection and counting function. We demonstrate experimentally that the combination of fused near-infrared phenomenology and fuzzy neural classification produces a robust solution to the problem of automatic vehicle occupant counting. We substantiate our argument by providing comparative experimental results for vehicle occupant counters based on visible, single near-infrared, and fused near-infrared bands. Our proposed solution can find a more general applicability as the basis for a reliable face detector both indoors and outdoors.

DETER: detection of events for threat evaluation and recognition

Abstract.The current security infrastructure can be summarized as follows: (1) Security systems act locally and do not cooperate in an effective manner, (2) Very valuable assets are protected inadequately by antiquated technology systems and (3) Security systems rely on intensive human concentration to detect and assess threats. In this paper we present DETER (Detection of Events for Threat Evaluation and Recognition), a research and development (R&D) project aimed to develop a high-end automated security system. DETER can be seen as an attempt to bridge the gap between current systems reporting isolated events and an automated cooperating network capable of inferring and reporting threats, a function currently being performed by humans. The prototype DETER system is installed at the parking lot of Honeywell Laboratories (HL) in Minneapolis. The computer vision module of DETER reliably tracks pedestrians and vehicles and reports their annotated trajectories to the threat assessment module for evaluation. DETER features a systematic optical and system design that sets it apart from “toy” surveillance systems. It employs a powerful Normal mixture model at the pixel level supported by an expectation-maximization (EM) initialization, the Jeffreys divergence measure, and the method of moments. It also features a practical and accurate multicamera calibration method. The threat assessment module utilizes the computer vision information and can provide alerts for behaviors as complicated as the “hopping” of potential vehicle thieves from vehicle spot to vehicle spot. Extensive experimental results measured during actual field operations support DETER’s exceptional characteristics. DETER has recently been successfully productized. The product-grade version of DETER monitors movements across the length of a new oil pipeline.

Two Examples of Indoor and Outdoor Surveillance Systems: Motivation, Design, and Testing

We examine the state of the security industry and market and underline the role that it plays in the R&D efforts. We also present a snapshot of the current state-of-the-art in indoor and outdoor surveillance systems for commercial applications. Then, we move on and describe in detail a prototype indoor surveillance system that we recently developed. The system is called Cooperative Camera Network (CCN) and reports the presence of a visually tagged individual throughout a building structure. Visual tagging is based on the color signature of a person. CCN is meant to be used for the monitoring of potential shoplifters in department stores. We also describe our prototype outdoor surveillance system, the DETER (Detection of Events for Threat Evaluation and Recognition). DETER can monitor large open spaces, like parking lots, and report unusual moving patterns by pedestrians or vehicles. To perform its function DETER fuses the field of views of multiple cameras into a sup er-view and performs tracking of moving objects across it. A threat assessment module with encoded suspicious behaviors performs the motion pattern identification. Both surveillance systems are good examples of technology transfer of state-of-the-art ideas from the research literature to the commercial domain. At the same time, they are good study cases for the extra engineering methodology and effort that is needed to adapt initial research concepts into a successful practical technology.

Day, night and all-weather security surveillance automation synergy from combining two powerful technologies

Thermal imaging is rightfully a real-world technology proven to bring confidence to daytime, nighttime and all weather security surveillance. Automatic image processing intrusion detection algorithms are also a real world technology proven to bring confidence to system surveillance security solutions. Together, day, night and all weather video imagery sensors and automated intrusion detection software systems create the real power to protect early against crime, providing real-time global homeland protection, rather than simply being able to monitor and record activities for post event analysis. These solutions, whether providing automatic security system surveillance at airports (to automatically detect unauthorized aircraft takeoff and landing activities) or at high risk private, public or government facilities (to automatically detect unauthorized people or vehicle intrusion activities) are on the move to provide end users the power to protect people, capital equipment and intellectual property against acts of vandalism and terrorism. As with any technology, infrared sensors and automatic image intrusion detection systems for global homeland security protection have clear technological strengths and limitations compared to other more common day and night vision technologies or more traditional manual man-in-the-loop intrusion detection security systems. This paper addresses these strength and limitation capabilities. False Alarm (FAR) and False Positive Rate (FPR) is an example of some of the key customer system acceptability metrics and Noise Equivalent Temperature Difference (NETD) and Minimum Resolvable Temperature are examples of some of the sensor level performance acceptability metrics.