- The system uses a large quantity of cameras that require monitoring for specific conditions or behaviors that are capable of being recognized.
- The setup and installation are relatively simple for the video analytics subsystem, which has high, sustained accuracy for the types of behaviors and objects recognized. With video synopsis or summarization, a condensed clip of all motion for selected criteria is continuously generated and stored, allowing an “instant review” of a readily available “video synopsis.” It is possible to summarize a 24-hour period of event entries in as little as 15 minutes, reducing incident-review time by at least 50 percent. Video analytics offering abnormal scene detection allows the user to set specific object criteria and direction. The scene is analyzed continuously, and abnormal behavior differing from the majority of the scene content is detected and annunciated or marked for later review.
- Video analytics embedded in the network camera represents a growing segment where applications run and values or decisions based on recognition are available with the “edge” network camera and minimal software.
One popular example in retail and quick-service establishments is the “people counter” where the network camera and built-in app return the number of people passing into a zone, through a boundary, or into the field of view. This can provide criteria on which to increase camera frame rate and stored resolution during the time of highest traffic.
Another popular video-recognition solution that runs either as an embedded network camera application or in the Video Management System is fixed License Plate Recognition and Capture (LPR/LPC). This specialized app captures license plate information for immediate processing by LPR software. The software may run in a rapid-acquisition mode and compare plates later against an approved list or perform the recognition sequentially as the vehicles pass within the camera field of view. In either case, LPR is a mature application embraced by law enforcement, electronic-toll collection, and parking management organizations; the trend to embed this function reduces cost and allows greater flexibility.
“Heat” activity mapping provides a visual color-coded summary showing how people have moved in the camera scene for a fixed duration. Useful in retail environments where “business intelligence” data is needed, this type of video content analysis can improve safety by analyzing the flow of pedestrian and vehicular traffic in a facility. Understanding personnel traffic flow will often help camera placement and ultimately the video forensic-review process.