Event cameras offer the exciting possibility of tracking the camera's pose during high-speed motion and in adverse lighting conditions. Despite this promise, existing event-based monocular visual odometry (VO) approaches demonstrate limited performance on recent benchmarks. To address this limitation, some methods resort to additional sensors such as IMUs, stereo event cameras, or frame-based cameras. Nonetheless, these additional sensors limit the application of event cameras in real-world devices since they increase cost and complicate system requirements. Moreover, relying on a frame-based camera makes the system susceptible to motion blur and HDR. To remove the dependency on additional sensors and to push the limits of using only a single event camera, we present Deep Event VO (DEVO), the first monocular event-only system with strong performance on a large number of real-world benchmarks. DEVO sparsely tracks selected event patches over time. A key component of DEVO is a novel deep patch selection mechanism tailored to event data. We significantly decrease the pose tracking error on seven real-world benchmarks by up to 97% compared to event-only methods and often surpass or are close to stereo or inertial methods. Code is available at https://github.com/tum-vision/DEVO

Devo Technology, the cloud-native logging and security analytics company, announced the expansion of its SciSec threat research team with the addition of two data science experts. Dr. Kevin Zhou will serve as Vice President of Data Science and is joined by Dr. Chaz Lever, Senior Director of Security Research. Zhou and Lever bring extensive experience in data science, machine learning and AI in both academia and industry to their new roles at Devo. Their combined expertise and leadership will be central to Devo's vision to deliver what the company calls the autonomous security operations center (SOC) – complete visibility, automation, augmented analytics, and open access to community expertise and content. In his role, Dr. Kevin Zhou will lead Devo's global data science team, spearheading machine learning and AI initiatives for the company.

Companies are quickly adopting cybersecurity products and systems that incorporate artificial intelligence (AI) and machine learning, but the technology comes with significant challenges, and it can't replace human analysts, experts say. In a Wakefield Research survey published this week, for example, almost half of IT security professionals (46%) said their AI-based systems create too many false positives to handle, 44% complained that critical events are not properly flagged, and 41% do not know what to do with AI outputs. In total, 89% of companies reported challenges with cybersecurity solutions that claimed to have AI capabilities. Not all AI-based projects are created equal, as some technology is more mature, says Gunter Ollmann, chief security officer at Devo, which sponsored the survey. "When they talk about rolling out AI for cybersecurity ... those are the projects that are commonly failing," he says.