Detecting relevant motion in a scene is a matter of finding the right balance between parameters. You can set the threshold for how large an object needs to be for the system to trigger an alert. You can decide how long a time the object needs to be moving in an image before it stops triggering the system. You can even decide how much an image can change before the system reacts. Advanced network cameras often allow you to place a number of different windows for motion detection within a viewing field. You can then define each window separately according to different parameters. This is particularly valuable when you know the viewing field regularly includes both stationary and mobile objects.
Camera tampering detection is another key pixel-based IV application incorporated in surveillance systems to protect the integrity of video transmissions. Without it, a camera's view can become obstructed or deliberately altered causing significant threats to go unnoticed and completely unusable video to be recorded and stored.
How it works: The tampering detection feature distinguishes the difference between expected changes in a camera view and unexpected changes due to tampering. It can detect whether the lens of the camera is being blocked by an errant tree branch or covered by paint, powder, moisture or a sticker. It can determine if the camera has been redirected to a view of no interest to security. It can even detect if a camera has been severely defocused or actually removed. Some camera tampering detection applications automatically learn the scene, making setup very easy.
Camera tampering detection is used most prevalent in environments prone to vandalism-such as schools, prisons or public transportation venues-where someone is more likely to intentionally redirect, damage or block cameras. Detection occurs in seconds, generating an alert to correct the situation. Without this ability, it could take months before surveillance operators discovered that a camera was pointed in the wrong direction and that archived footage was useless.
Pixel-based image enhancement
Weather conditions naturally affect the quality of video images in outdoor surveillance. Fog, smoke, rain and snow create a challenge for security personnel to effectively monitor scenes and adequately identify people, objects and activity. Adjusting brightness and contrast do little to improve image clarity. With advanced intelligent video algorithms, however, you can analyze video streams and detect typical distortions caused by bad weather. The software applies real-time enhancements to the image, restoring it as much as possible to what it would have looked like without the distortion. The result is a video stream with substantially improved image quality.
Pointers for real-world deployment
To ensure that your pixel-based intelligent video application works accurately you need to address three factors: the video image quality, the efficiency of the advanced algorithms, and the processing power required.
- Video image quality. High frame rates and high resolution are not necessary. Pixel-based analysis functions quite well at five to 10 frames per second and in many applications CIF resolution (about 0.1 megapixels) is sufficient. Camera placement is critical though.
- Algorithm efficiency. The only way to assess the quality of an IV algorithm is to field test the application under realistic conditions. Run a pilot in the environment to determine how fast it is and how many correct responses and false alarms it generates. The number of acceptable errors or missed "true positives" will depend on the safety and security concerns of the user.
- Computer processing power. Since IV applications are mathematically complex, they require more computing power. How well they perform depends heavily on the processors used and the amount of memory available. The more processing power available, the faster they will be able to perform.
Setting realistic expectations
It is important to understand that IV applications are not infallible. But with careful adherence to some best practices, users can achieve between 90 and 95 percent accuracy. When deploying this technology, you need to set realistic expectations. Reaching 95 percent accuracy is very challenging; achieving 99 percent or beyond can be extremely difficult and costly in a real-world environment. Configuring a system is a delicate balance between not missing essential situations and reducing false triggers. Monitor and adjust your configuration over a 24-hour period to account for changes in lighting that might impact results. Understand that the more parameters you adjust in an application, the longer it will take to optimize-anywhere from a day to several weeks. The cost of the IV system will also have to be weighed against other surveillance alternatives, such as employing more security personnel. And accept that no system will ever be 100 percent accurate.