Given the key role that surveillance video played in the aftermath of the Boston bombing, I’ve been giving some thought to how present and future surveillance technologies could more quickly and efficiently investigate such a tragedy.
What law enforcement officials were able to quickly discern from the pile of video footage with the tools they had available is truly amazing. I can only imagine the number of hours expended on manual video review.
Two areas strike me as being appropriate to consider, and they both involve video analytics. The first area is identifying suspicious activity. Traditional video analytics provide tools such as motion, direction, speed, loitering, object left behind and size. With these, one could ask: Is anybody in an area walking quickly relative to their neighbors? Is there erratic movement? Is there an unusual width to height ratio (suggesting a large backpack, or any backpack)? Was something put down and left behind? Did that placement follow a period of loitering?
While none of these answers would be conclusive in themselves, they appear to be the types of issues that video investigators would look for.
While it may be a challenge, behavior-based analytics from companies such as BRS Labs (Request more info: www.securityinfowatch.com/10482504), might apply. Behavioral analytics looks for the abnormal, but it takes time to stabilize in order to define “normal.” Scenes that are brand new and involve constant change would be a challenge; but, what if a system could determine average speed, define average or lower speed as normal, and flag anything moving faster? Or, what if it could determine that a certain size backpack exceeded the threshold for normal? It’s not that you would alarm on such events, but rather mark them for further review. Ideally, you would mark them for the duration the subject was in the camera view.
Further, data could be correlated with non-video sensors. John Convy of BRS tells me that all of this is feasible. This could be especially powerful if coupled with video synopsis techniques.
Another approach parallels what was discussed for the use of facial analysis at Super Bowl XXXV (Tampa, 2001). Take subjects of interest and compare them with a database to evolve a match.
Several issues immediately arise: How good are the video images? Can they be converted to frontal poses for face matching? What and how relevant is the database? If you could achieve some number of valid matches, it would constitute a set of candidates for investigators, and you could correlate those with results from traditional analytics to further narrow that set. Certainly, you would want to be able to mark that video once a facial match was achieved to allow for later investigation.
Better Technology Means Better Investigations
Advances in technology and actual city surveillance deployments will help. Start with the cameras — in Boston, many cameras that just happened to be in the vicinity of the bombings provided the bulk of the video footage for review. Whether they were for building surveillance, or news, or whatever, the cameras were likely not optimal for use with analytics. Megapixel cameras with rich analytic features and embedded storage, thoughtfully placed and with appropriate lenses, would be required.
The video network could be supplemented with CBRN (Chemical, Biological, Radiological and Nuclear) sensors for added coverage. Already on the way are enhanced analytic and processing capabilities for the edge cameras, providing the means to mark the video with metadata, allowing later review or use with Big Data systems.
With facial recognition, there are several issues to be addressed beyond cameras. According to Joe Rosenkrantz, CEO of FaceFirst, “Both software technologies and choice of hardware, consciously deployed, are required for a successful system.”