Video Motion Detection
Video Motion Detection – The human brain has limited attention span capabilities. A study found that after 20 minutes, guards watching a video scene will miss up to 95 percent of all activity. Leveraging advancements in video pattern detection, video analytics technology addresses issues and has comprehensively evolved from being a strictly forensic tool into a powerful proactive solution. Paired with high-definition imaging, HD provides security operators with highly accurate alerts and clear image detail, enhancing their ability effectively intervene and take action when an incident occurs.
Artificial intelligence pioneer, Herb Simon stated, “A wealth of information creates a poverty of attention.” Simon noted that most technology systems were focused on providing as much information as possible without taking the human attention span into consideration. Consequently, these systems provided a surplus of information to people, when what was needed were systems that filtered out irrelevant information and, highlighted items of interest.
What Herb Simon was describing was the theory of attention economics; an approach to the management of information that treats human attention as a Scarce commodity and a limiting factor in the absorption of information. The attention economics theory supports the creation of systems attention capabilities into consideration in their design, creating filters to ensure the first content a user is presented with is relevant and of interest.
Based on the theory of attention economics, most security control centers and corresponding Video surveillance systems today present security personnel with a wealth of information, leading to a poverty of attention. The aforementioned study showed a disturbing trend in operator performance:
1. Security operator performance degrades considerably after 20 minutes.
2. Poor image quality accelerates this rate of degradation.
3. Viewing twice the number of cameras accelerates degradation by a factor of two.
The concept of video analytics technology is to present only the information that will require an operator’s immediate attention. However, the vast majority of these systems create a disproportionate amount of irrelevant information contributing to operator confusion and inaction.
Video analytics has evolved across a series of three technologies:
1. Video Motion Detection – any change from one frame to another is important.
2. Advanced Video Motion Detection – any change that deviates from the background model is important.
3. Advanced Video Pattern Detection – Any change that has a pattern of a known object type is important.
Video Motion Detection (“VMD”) is now a standard feature included with most new surveillance cameras, recorders, and video management software. The VMD feature focuses on detecting any pixel movement from scene to scene based on a simplistic user-defined threshold. VMD is most effective in sterile and static environments. However, the technology is limited in dynamic environments, resulting in high false alarm rates. Unfortunately, this high rate of false alarms leads directly to a rapid decrease in operator attention.
In response to this limitation, the industry then progressed from VMD to Advanced Video Motion Detection (“AVMD”). AVMD is based on background modeling, alerting on any change that deviates from an established background model. This technology focuses on monitoring a scene and using the data captured via complex manual calibration to identify moving objects. AVMD is effective when set up and calibrated correctly, yet is limited when background composition changes e.g. environmental, seasonal, and physical changes), increasing false alarm rates over time and initiating the need for regular recalibration.
The latest evolution in video analytics is Advanced Video Pattern Detection, which is based on pattern modeling algorithms, alerting on any change that has a pattern of a known object type such as a person or a vehicle. The technology focuses on recognizing the objects in view and using the information of the movement of the object to accurately classify it. Consider how humans recognize objects: we recognize an object based on its look, shape, and movement. Advanced Video Pattern Detection works in a similar fashion.
Of the three types of video analytic technologies noted above, Advanced Video Pattern Detection typically provides the lowest rate of false alarms, helping to sustain operator attention by highlighting information that is relevant and of interest.
The combination of high-definition video and analytics:
In a study on visual sustained attention, data showed a strong correlation between image quality and sustained attention, where lower video quality translated into a decrease in sustained attention. Within the security industry, security operators cannot see what is not captured, and inevitably it is impossible to enhance low-resolution images, thus highlighting the importance of high-quality video.
With the evolution of high-definition video surveillance, the use of higher resolution and higher quality video can be an effective tool to increase sustained operator attention.
A study on the effectiveness of human video surveillance performance indicated there are severe addresses to the challenge of increased information with decreased attention spans. We are now in the era of high-definition video imaging. HD analytics paired with high-definition imaging provides security operators with highly accurate alerts and clear image detail, giving them the ability to effectively intervene in a situation of interest and take action.