Dual-sensor with 360° IR and deep learning
This dual-sensor, multidirectional camera delivers excellent image quality even in challenging light conditions. Built on ARTPEC-8, it offers support for advanced analytics based on deep learning on the edge.
AXIS P4705-PLVE includes 360° IR illumination with individually controllable LEDs so you’ll enjoy clear images even in low light or complete darkness as well as undistorted colors during the day. Featuring AXIS Lightfinder you get color images recorded in near darkness. And Forensic WDR ensures clarity when there’s both dark and light areas in the scene. It offers two 2 MP channels and a frame rate of 30 fps letting you capture both wide angle and zoomed-in, detailed views. And, thanks to 2.5x zoom it delivers wide views and you can zoom in for all the details. Furthermore, audio input allows you to listen in to transactions or incidents.
This compact, multidirectional camera is perfect when you need to see in two directions simultaneously. It offers flexible positioning of both varifocal camera heads and each head can be controlled individually so you can monitor scenes in two directions in wide or zoomed-in views. The camera’s sensors can also be twisted, allowing two camera heads to be positioned closer together. This is the perfect setup for straight hallways or at the corners of an intersection where it can look in both directions simultaneously.
Built on ARTPEC-8, this high-performance camera includes a deep learning processing unit (DLPU). This enables improved processing and storage capabilities allowing you to collect and analyze even more data than before—on the edge. Plus, it delivers valuable metadata facilitating fast, easy, and efficient forensic search capabilities in live or recorded video. Thanks to AXIS Object Analytics, AXIS P4705-PLVE can detect and classify humans, vehicles, and types of vehicles—all tailored to your specific needs. Furthermore, with support for ACAP version 4, you can add value to your system with tailor-made applications based on deep learning on the edge.
