Grant allows SAFR to extend AI-powered analytics to unmanned ground vehicle (UGV) and reduce inherent risk in perimeter protection and domestic emergency medical services (EMS) search and rescue missions
Seattle, WA, April 26, 2021 (GLOBE NEWSWIRE) — SAFR® from RealNetworks, Inc. (NASDAQ: RNWK), a face recognition and visual analytics platform specializing in computer vision optimized for real-world challenges, announced it was awarded its third Small Business Innovation Research (SBIR) contract. This contract enables SAFR to advance its computer vision platform to support perimeter protection and domestic search and rescue missions.
The SBIR contract allows SAFR to enhance its platform to run on an NVIDIA® Jetson AGX Xavier™ based UGV system. The addition of SAFR’s AI-powered analytics on autonomous and semi-autonomous robotic devices provides a force multiplier and a layer of safety for defense and civilian security and emergency services personnel.
“As a USAF military working dog handler, I have employed canines in various environments fulfilling the multi-use role of detection and deterrence. The ability to utilize UGV systems to augment K9 teams during work/rest cycles, or as an additional force, broadens security in-depth and allows operations to continue unhindered,” said Air Force Tech. Sgt. Dustin Cain, Non-Commissioned Officer in Charge of Police Services, 366th Security Forces Squadron, Mountain Home Air Force Base, Idaho.
SAFR’s focus is to reduce the risk service members face in their daily duties by allowing SAFR-enhanced UGVs to perform perimeter patrols and monitor contested environments. The UGV platform will recognize when unauthorized persons are present in restricted areas. SAFR’s solution is being extended to operate onboard UGV devices and produce patrol reports with positional data upon completion of patrol duties. Critical events can trigger a real-time alert based on mission requirements.
In high-risk scenarios involving search and rescue missions, such as searching collapsed and unstable buildings for survivors following an earthquake, the robotic device could locate persons using SAFR’s body detection capabilities, providing real-time alerts and access route details to speed up extraction – greatly reducing the risk for responders. Currently, no other UGV has advanced detection and analytic capabilities integrated for military and civilian EMS use.
SAFR can operate entirely on the UGV, providing post-patrol reports in network denied environments. Alternately in degraded or persistent network environments, a SAFR equipped UGV could stream intel in real-time as it develops or provide burst transmission when appropriate.
“We see this as an incredible opportunity to demonstrate how AI is a force multiplier and can be used to reduce risk to security forces and emergency responders,” said Eric Hess, Senior Product Manager at SAFR. “The UGV platform equipped with SAFR AI represents the future of embedded device operations and computer vision for an emerging range of robotic systems.”
SAFR from RealNetworks (https://safr.com) is a high-performance computer vision platform. With fast, accurate, unbiased face recognition and additional face- and person-based AI features, SAFR leverages the power of AI to enhance security and convenience for our customers around the globe. Specializing in touchless secure access, real-time video surveillance, and digital identity authentication, SAFR is optimized to run on virtually any camera or camera-enabled device. Deploy as a standalone solution, integrated with leading video management systems, or directly on your device running on the edge for greater situational awareness and insights to improve operational efficiency. SAFR is headquartered in Seattle, WA, USA with offices around the world.
© 2021 RealNetworks and SAFR are registered trademarks of RealNetworks, Inc. All other trademarks, names of actual companies and products mentioned herein are the property of their respective owners.
For more information please contact:
Veronique Froment SAFR 603-537-9248 email@example.com