Automated Behavior Analysis

Over the last several years, single actor terrorism has introduced a new phrase to the American public, self-radicalization. As an anonymous threat, the whereabouts, motives and activities of self-radicalized extremists are virtually unknown. Although investigative arms of the U.S. Government have uncovered several terrorist plots over the years , those perpetrated by self-radicalized extremists often go undetected until it is too late. The 1995 bombing of the Alfred P. Murrah Federal Building, the 2013 Boston Marathon bombing and the 2015 San Bernardino shootings are just a few examples of attacks perpetrated by self-radicalized extremists in recent history. Their existence poses a challenging security question for government officials; how do you detect an anonymous threat? The 2013 Boston Marathon bombing demonstrated the benefits of public and private video surveillance. In the aftermath of this horrific event, authorities, attempting to identify those responsible, amassed an astonishing “120,000 still photos and nearly 13,000 video clips” (Kiger, 2014, p. NA). As the race to identify the perpetrators continued, a segment of video was discovered that showed “a backpack-carrying man in a white hat talking on a cell phone. As the crowd
By automatically analyzing human behavior and alerting authorities to only those instances that require higher levels of analysis, the wasted man hours and human error associated with active monitoring are effectively mitigated by way of a reduction in the amount of video monitored. Additionally, passive monitoring becomes more active by automatically analyzing human behavior in the background. As a result, automated behavior analysis could effectively increase public safety through a reduction in criminal activity by identifying suspicious or abnormal behavior “during the suspicion stage itself” (Gowsikhaa, Abirami, & Baskaran, 2012, p.