The Current Status of Motion Detection, Event Detection and Situation Awareness - Thesis Example

Currently, event detection has become more sophisticated with recent tools being put in place to analyze the ever rising volumes of video from various sources such as video surveillance from closed-circuit cameras (CCTV’s). The long hours of video footage have to be analyzed for security purposes by security personnel, which usually requires long man-hours and is prone to errors and personal judgment. There is a need to develop computer software that can do the same analysis more efficiently and precisely in real-time and provide feedback to the management.

Today's motion-detecting cameras all make use of variations of the same technique for detecting scene displacements. Ideally, each successive picture is mapped to a reference image pixel by pixel. In case the number of pixels that do not resemble goes beyond some preset threshold, the picture is interpreted by the computer as different from the reference image and therefore reported as anomaly or motion is detected and the alarm is set. Normally this requires considerable picture preprocessing to minimize random noise from dark currents of CCD and other sources.

Is the pixel by pixel separation between the picture at hand and the reference image, the factor r j is a noise minimization term, and e is the sensitivity threshold. There exist many different versions of this process that have been implemented. However, it is not the only motion that can cause sufficient disparities to generate an alarm. The camera also detects and records all scene movements; a lot of which are incidental to the real surveillance and do not have safeguard impacts (Ke, Sukthankar, and Hebert).

Nevertheless, in spite of these limitations, today’s FEMD technology has significantly minimized the sizes of data that must be gathered and reviewed as opposed to traditional time-lapse surveillance techniques. Smart Videos attempt to minimize both false alarms and incidental alarms by widening the FEMD process to accommodate changes to the geometry of the field of perception; and then employ the geometry information to sort and identify moving objects (Kulkarni, Ganesan, and Shenoy).

A larger part of the prior works to detect and sort objects was primarily geared towards satellite imagery]. The methods developed for this application are reliant on certain scope conditions that are found in this scenario; like fixed vertical aspect and minimal motion. There was no bid to actually detect and monitor motion in the sense of Safeguards, and the layout of the objects of interest was considerably constant. A lot of recent work has been based on texture-mapping within color-space; which is not available in imagery Safeguards.

Many developments have been made in towards the aim of automatically tracking, identifying, and separating objects of interest in surveillance. A lot of work still remains incomplete to achieve this goal, and Smart Video is not expected to rise as a long term technology until the end of the century. However, the outcomes to date already give potential benefits in remote monitoring Safeguards environment today (Gnawali, Jang and Paek). The demonstrated capability to ascertain that a scene displacement was not due to lighting inconsistencies, but was indeed as a result of the movement of a discrete object in the area of view has the specific potential to eliminate or reduce the false alarm rate.

In crucial complex well defined Safeguards scenarios it might be possible to discriminate between a small size, quick, vertical object and a large size, slow, horizontal object reliably (Gnawali, Jang and Paek). This would enable the system to weigh out incidental motion alarms that have no Safeguards sensible information. all of these capabilities can minimize the amount of data to be gathered and reviewed; and may provide a means of minimizing the periods an inspector should be on site at the time of activities.

The major challenge in deployment of these methods is the frame-rate of now available authenticating cameras. It is projected, however, that this challenge will be easier and quick to face out than was the processing technology development (Gnawali, Jang and Paek). Incorporation of a situational awareness and consistent tracking program is becoming a basic function and, in some scenarios a must, for IT Security organizations currently. "Situational awareness" is defined by a Chief Information Security Officer (CSO) working group as, "is having access to near real-time information that accurately describes the security posture of the organization with drill down capability to a single actor, system or application.

Information includes sufficient knowledge about: threats, vulnerabilities, resources/assets, and the significance of the interactions between the three components" (Governance and Risk Solutions (2011) To attain a strategic level of security risk management in enterprises, a top-down integration of existing security features and viable datasets is essential (Nam, Abe and Kamwata). The relation and analysis of that information provides insight into an enterprises security risk status. Awareness is aired by alerts, summary, metrics, details, and narrative reporting present in tailored dashboard views.

Many security solutions currently provide a tactical view into critical areas of the organization, centering on people, asset, or information. Risk vision relates data from prior security and IT tools merge that with enterprises, compliance, and risk information to portray a detailed and risk optimized understanding of security posture, at the business large picture level. With a wide range of out-of-the-box relations to eminent security and IT management systems and firm automation technologies relying on open standards such as SCAP, Risk vision may automate large amount of security data in real-time to develop true situational awareness intelligence (Walker).

Ocularis from On-Net Surveillance Systems (OnSSI) has been upgraded to put management of physical security data even more powerfully in the users’ hands (Ke, Sukthankar and Hebert). It now enables advanced event detection, shared alert management, bookmarking and transfer of evidence in less time.