Motion detection is an important element of many, if not, most surveillance systems. It plays a central role in both storage search time reduction. Storage is routinely reduced by 30% - 80% by using motion based rather than continuous recording. Likewise, an investigator can often much faster find a relevant event by simply scanning through areas of motion rather than watching through all video.
At the same time there are a number of challenges associated with using motion detection:
- Scene Conditions: The accuracy of motion detection and the amount of times motion is detected can vary depending on what's in the scene - people, cars, trees, leaves, etc. - and the time of day - night time with lots of noise, sunrise and sunset with direct sunlight into a camera, etc.
- Performance of Detector: Motion detetion is built into many surveillance products - from DVRs to VMS systems and now IP cameras. As such, how well each one works can vary significantly.
In this report, we share our results from a series of tests we performed to better understand motion detection performance.
We did a series of tests in different locations:
- Indoor well light scene to simulate the simplest scene possible
- Indoor dark scene (<1 lux) to examine what problems low light caused
- Outdoor parking lot to see how a complex scene with trees, cars and people would perform
- Roadway to see how a moderately complex scene with periodic cars would perform
Three IP cameras were used with their motion detection enabled to see differences in performance:
With these tests, we answered the following questions:
- How can one estimate motion percentage accurately?
- Does motion estimation vary significantly by scene?
- How accurate was motion detection in each scene?
- Did certain cameras exhibit greater false motion detection than others? What scenes or conditions drove those problems?